data.yaml

section:
- title: fl-on-graph-data-and-graph-neural-network
- title: private-graph-neural-networks
- title: fl-on-tabular-data
- title: fl-in-top-tier-journal
- title: fl-in-top-ai-conference-and-journal
- title: fl-in-top-ml-conference-and-journal
- title: fl-in-top-dm-conference-and-journal
- title: fl-in-top-secure-conference-and-journal
- title: fl-in-top-cv-conference-and-journal
- title: fl-in-top-nlp-conference-and-journal
- title: fl-in-top-ir-conference-and-journal
- title: fl-in-top-db-conference-and-journal
- title: fl-in-top-network-conference-and-journal
- title: fl-in-top-system-conference-and-journal
- title: federated-learning-framework
fl-on-graph-data-and-graph-neural-network:
  header:
    title: 'Title                                                        '
    affiliation: ' Affiliation                                   '
    venue: ' Venue                                                 '
    year: ' Year '
    materials: ' Materials'
  length:
    title: 60
    affiliation: 45
    venue: 53
    year: 4
    materials: 60
  body:
  - title: 'Federated Visualization: A Privacy-Preserving Strategy for Aggregated
      Visual Query.'
    affiliation: ZJU
    venue: 'IEEE Trans. Vis. Comput. Graph. :mortar_board:'
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10083324
      PDF: https://arxiv.org/abs/2007.15227
  - title: Personalized Subgraph Federated Learning
    affiliation: KAIST
    venue: 'ICML :mortar_board:'
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2206.10206
  - title: Semi-decentralized Federated Ego Graph Learning for Recommendation
    affiliation: SUST
    venue: 'WWW:mortar_board:'
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583337
      PDF: https://arxiv.org/abs/2302.10900
  - title: Federated Graph Neural Network for Fast Anomaly Detection in Controller
      Area Networks
    affiliation: ECUST
    venue: 'IEEE Trans. Inf. Forensics Secur. :mortar_board:'
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10026810
  - title: Federated Learning Over Coupled Graphs
    affiliation: XJTU
    venue: 'IEEE Trans. Parallel Distributed Syst. :mortar_board:'
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10029878
      PDF: https://arxiv.org/abs/2301.11099
  - title: 'HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity
      in Horizontal Federated Learning'
    affiliation: Nankai University
    venue: 'IEEE Trans. Vis. Comput. Graph. :mortar_board:'
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9912364
      PDF: https://arxiv.org/abs/2208.07491
  - title: Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
    affiliation: UTS
    venue: 'AAAI :mortar_board:'
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.13009
      CODE: https://github.com/yuetan031/fedstar
  - title: 'FedGS: Federated Graph-based Sampling with Arbitrary Client Availability'
    affiliation: XMU
    venue: 'AAAI :mortar_board:'
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.13975
      CODE: https://github.com/wwzzz/fedgs
  - title: An Information Theoretic Perspective for Heterogeneous Subgraph Federated
      Learning.
    affiliation: PKU
    venue: DASFAA
    year: '2023'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-031-30637-2_50
  - title: 'GraphCS: Graph-based client selection for heterogeneity in federated learning'
    affiliation: NUDT
    venue: J. Parallel Distributed Comput.
    year: '2023'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S0743731523000394?via%3Dihub
  - title: 'Towards On-Device Federated Learning: A Direct Acyclic Graph-based Blockchain
      Approach'
    affiliation: BUPT
    venue: IEEE Trans. Neural Networks Learn. Syst.
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9524833
      PDF: https://arxiv.org/abs/2104.13092
  - title: Short-Term Traffic Flow Prediction Based on Graph Convolutional Networks
      and Federated Learning
    affiliation: ZUEL
    venue: IEEE Trans. Intell. Transp. Syst.
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9794333
  - title: Hyper-Graph Attention Based Federated Learning Methods for Use in Mental
      Health Detection.
    affiliation: HVL
    venue: IEEE J. Biomed. Health Informatics
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9767700
  - title: Federated Learning-Based Cross-Enterprise Recommendation With Graph Neural
    affiliation: ''
    venue: IEEE Trans. Ind. Informatics
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9873989
  - title: 'Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical
      Federated Learning'
    affiliation: ZJUT
    venue: IEEE Trans. Comput. Soc. Syst.
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9745270
      PDF: https://arxiv.org/abs/2110.06468
      CODE: https://github.com/hgh0545/graph-fraudster
  - title: 'ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.'
    affiliation: ''
    venue: Peer Peer Netw. Appl.
    year: '2023'
    materials:
      PUB: https://link.springer.com/article/10.1007/s12083-023-01472-2
  - title: 'FedCKE: Cross-Domain Knowledge Graph Embedding in Federated Learning'
    affiliation: SWJTU
    venue: IEEE Trans. Big Data
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9887815
  - title: 'Asynchronous federated learning with directed acyclic graph-based blockchain
      in edge computing: Overview, design, and challenges.'
    affiliation: ''
    venue: Expert Syst. Appl.
    year: '2023'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S0957417423003974?via%3Dihub
  - title: 'FedGR: Federated Graph Neural Network for Recommendation System'
    affiliation: CUPT
    venue: Axioms
    year: '2023'
    materials:
      PUB: https://www.mdpi.com/2075-1680/12/2/170
  - title: 'S-Glint: Secure Federated Graph Learning With Traffic Throttling and Flow
      Scheduling.'
    affiliation: ''
    venue: IEEE Trans. Green Commun. Netw.
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9810521
  - title: 'FedAGCN: A traffic flow prediction framework based on federated learning
      and Asynchronous Graph Convolutional Network'
    affiliation: ''
    venue: Appl. Soft Comput.
    year: '2023'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S156849462300193X?via%3Dihub
  - title: 'GDFed: Dynamic Federated Learning for Heterogenous Device Using Graph
      Neural Network'
    affiliation: KHU
    venue: ICOIN
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10048926
      CODE: https://github.com/IntelligentNetworkingLAB/Graph-Neural-Network-based-Federated-Learning-for-Heterogenous-Device-Network
  - title: Coordinated Scheduling and Decentralized Federated Learning Using Conflict
      Clustering Graphs in Fog-Assisted IoD Networks
    affiliation: UBC
    venue: IEEE Trans. Veh. Technol.
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9932020
  - title: 'FedRule: Federated Rule Recommendation System with Graph Neural Networks'
    affiliation: CMU
    venue: IoTDI
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3576842.3582328
      PDF: https://arxiv.org/abs/2211.06812
  - title: 'FedWalk: Communication Efficient Federated Unsupervised Node Embedding
      with Differential Privacy'
    affiliation: SJTU
    venue: 'KDD :mortar_board:'
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539308
      PDF: https://arxiv.org/abs/2205.15896
  - title: 'FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform
      for Federated Graph Learning :fire:'
    affiliation: Alibaba
    venue: 'KDD (Best Paper Award) :mortar_board:'
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2204.05562
      CODE: https://github.com/alibaba/FederatedScope
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539112
  - title: Deep Neural Network Fusion via Graph Matching with Applications to Model
      Ensemble and Federated Learning
    affiliation: SJTU
    venue: 'ICML :mortar_board:'
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/liu22k/liu22k.pdf
      CODE: https://github.com/Thinklab-SJTU/GAMF
  - title: Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the
      Federated Setting **`kg.`**
    affiliation: ZJU
    venue: 'IJCAI :mortar_board:'
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/273
      PDF: https://doi.org/10.48550/arXiv.2205.04692
      CODE: https://github.com/zjukg/maker
  - title: Personalized Federated Learning With a Graph
    affiliation: UTS
    venue: 'IJCAI :mortar_board:'
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/357
      PDF: https://arxiv.org/abs/2203.00829
      CODE: https://github.com/dawenzi098/SFL-Structural-Federated-Learning
  - title: Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
    affiliation: ZJU
    venue: 'IJCAI :mortar_board:'
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/272
      PDF: https://arxiv.org/abs/2005.11903
  - title: 'SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural
      Networks on Molecular Data'
    affiliation: USC
    venue: 'AAAI:mortar_board:'
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20643
      PDF: https://arxiv.org/abs/2106.02743
      CODE: https://github.com/FedML-AI/SpreadGNN
      解读: https://zhuanlan.zhihu.com/p/429720860
  - title: 'FedGraph: Federated Graph Learning with Intelligent Sampling'
    affiliation: UoA
    venue: 'TPDS :mortar_board:'
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/abstract/document/9606516/
      CODE: https://github.com/cfh19980612/FedGraph
      解读: https://zhuanlan.zhihu.com/p/442233479
  - title: 'Federated Graph Machine Learning: A Survey of Concepts, Techniques, and
      Applications **`surv.`**'
    affiliation: University of Virginia
    venue: SIGKDD Explor.
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3575637.3575644
      PDF: https://arxiv.org/abs/2207.11812
  - title: 'Semantic Vectorization: Text- and Graph-Based Models.'
    affiliation: IBM Research
    venue: Federated Learning
    year: '2022'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-96896-0_3
  - title: 'GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification
      on Graphs'
    affiliation: IIT
    venue: ICDM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10027604
      PDF: https://arxiv.org/abs/2012.04187
      解读: https://zhuanlan.zhihu.com/p/431479904
  - title: 'More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural
      Networks'
    affiliation: TU Delft
    venue: ACSAC
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3564625.3567999
      PDF: https://arxiv.org/abs/2202.03195
  - title: 'FedNI: Federated Graph Learning with Network Inpainting for Population-Based
      Disease Prediction'
    affiliation: UESTC
    venue: TMI
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9815303
      PDF: https://arxiv.org/abs/2112.10166
  - title: 'SemiGraphFL: Semi-supervised Graph Federated Learning for Graph Classification.'
    affiliation: PKU
    venue: PPSN
    year: '2022'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-031-14714-2_33
  - title: Federated Spatio-Temporal Traffic Flow Prediction Based on Graph Convolutional
      Network
    affiliation: TJU
    venue: WCSP
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10039323
  - title: A federated graph neural network framework for privacy-preserving personalization
    affiliation: THU
    venue: Nature Communications
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41467-022-30714-9
      CODE: https://github.com/wuch15/FedPerGNN
      解读: https://zhuanlan.zhihu.com/p/487383715
  - title: Malicious Transaction Identification in Digital Currency via Federated
      Graph Deep Learning
    affiliation: BIT
    venue: INFOCOM Workshops
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9797992/
  - title: Efficient Federated Learning on Knowledge Graphs via Privacy-preserving
      Relation Embedding Aggregation **`kg.`**
    affiliation: Lehigh University
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.findings-emnlp.43/
      PDF: https://arxiv.org/abs/2203.09553
      CODE: https://github.com/taokz/FedR
  - title: Power Allocation for Wireless Federated Learning using Graph Neural Networks
    affiliation: Rice University
    venue: ICASSP
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9747764
      PDF: https://arxiv.org/abs/2111.07480
      CODE: https://github.com/bl166/wirelessfl-pdgnet
  - title: 'Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot
      Linear Mixing Approach Inspired By Graph Regularization'
    affiliation: UC
    venue: ICASSP
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9746007
      PDF: https://www.math.ucla.edu/~harlin/papers/mtl.pdf
      CODE: https://github.com/HarlinLee/multitask-fusion
  - title: Graph-regularized federated learning with shareable side information
    affiliation: NWPU
    venue: Knowl. Based Syst.
    year: '2022'
    materials:
      PUB: https://www.sciencedirect.com/science/article/pii/S095070512201053X
  - title: Federated knowledge graph completion via embedding-contrastive learning
      **`kg.`**
    affiliation: ZJU
    venue: Knowl. Based Syst.
    year: '2022'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S0950705122007316?via%3Dihub
  - title: Federated Graph Learning with Periodic Neighbour Sampling
    affiliation: HKU
    venue: IWQoS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9812908
  - title: 'FedGSL: Federated Graph Structure Learning for Local Subgraph Augmentation.'
    affiliation: ''
    venue: Big Data
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10020771/
  - title: Domain-Aware Federated Social Bot Detection with Multi-Relational Graph
      Neural Networks.
    affiliation: UCAS; CAS
    venue: IJCNN
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9892366
  - title: A Federated Multi-Server Knowledge Graph Embedding Framework For Link Prediction.
    affiliation: ''
    venue: ICTAI
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10097981
  - title: A Privacy-Preserving Subgraph-Level Federated Graph Neural Network via
      Differential Privacy
    affiliation: Ping An Technology
    venue: KSEM
    year: '2022'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-031-10989-8_14
      PDF: https://arxiv.org/abs/2206.03492
  - title: Clustered Graph Federated Personalized Learning.
    affiliation: NTNU
    venue: IEEECONF
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10051979
  - title: 'FedGCN: Convergence and Communication Tradeoffs in Federated Training
      of Graph Convolutional Networks'
    affiliation: CMU
    venue: CIKM Workshop (Oral)
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2201.12433
      CODE: https://github.com/yh-yao/FedGCN
  - title: Investigating the Predictive Reproducibility of Federated Graph Neural
      Networks using Medical Datasets.
    affiliation: ''
    venue: MICCAI Workshop
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2209.06032
      CODE: https://github.com/basiralab/reproducibleFedGNN
  - title: Peer-to-Peer Variational Federated Learning Over Arbitrary Graphs
    affiliation: UCSD
    venue: Int. J. Bio Inspired Comput.
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9825726/
  - title: Federated Multi-task Graph Learning
    affiliation: ZJU
    venue: ACM Trans. Intell. Syst. Technol.
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3527622
  - title: Graph-Based Traffic Forecasting via Communication-Efficient Federated Learning
    affiliation: SUSTech
    venue: WCNC
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9771883
  - title: Federated meta-learning for spatial-temporal prediction
    affiliation: NEU
    venue: Neural Comput. Appl.
    year: '2022'
    materials:
      PUB: https://link.springer.com/article/10.1007/s00521-021-06861-3
      CODE: https://github.com/lwz001/FML-ST
  - title: 'BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning'
    affiliation: NTU
    venue: IEEE Transactions on Big Data
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/abstract/document/9832778
      PDF: https://fl-icml.github.io/2021/papers/FL-ICML21_paper_74.pdf
  - title: Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning
    affiliation: Missouri S&T
    venue: INFCOM Workshops
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9798077
  - title: Federated learning of molecular properties with graph neural networks in
      a heterogeneous setting
    affiliation: University of Rochester
    venue: Patterns
    year: '2022'
    materials:
      PUB: https://linkinghub.elsevier.com/retrieve/pii/S2666389922001180
      PDF: https://arxiv.org/abs/2109.07258
      CODE: https://doi.org/10.5281/zenodo.6485682
  - title: Graph Federated Learning for CIoT Devices in Smart Home Applications
    affiliation: University of Toronto
    venue: IEEE Internet Things J.
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9983539
      PDF: https://arxiv.org/abs/2212.14395
      CODE: https://github.com/FL-HAR/Graph-Federated-Learning-for-CIoT-Devices.git
  - title: Multi-Level Federated Graph Learning and Self-Attention Based Personalized
      Wi-Fi Indoor Fingerprint Localization
    affiliation: SYSU
    venue: IEEE Commun. Lett.
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9734052
  - title: Graph-Assisted Communication-Efficient Ensemble Federated Learning
    affiliation: UC
    venue: EUSIPCO
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9909826
      PDF: https://arxiv.org/abs/2202.13447
  - title: Decentralized Graph Federated Multitask Learning for Streaming Data
    affiliation: NTNU
    venue: CISS
    year: '2022'
    materials:
      PUB: https://doi.org/10.1109/CISS53076.2022.9751160
  - title: Neural graph collaborative filtering for privacy preservation based on
      federated transfer learning
    affiliation: ''
    venue: Electron. Libr.
    year: '2022'
    materials:
      PUB: https://www.emerald.com/insight/content/doi/10.1108/EL-06-2022-0141/full/html
  - title: Dynamic Neural Graphs Based Federated Reptile for Semi-Supervised Multi-Tasking
      in Healthcare Applications
    affiliation: Oxford
    venue: JBHI
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9648036
  - title: 'FedGCN: Federated Learning-Based Graph Convolutional Networks for Non-Euclidean
      Spatial Data'
    affiliation: NUIST
    venue: Mathematics
    year: '2022'
    materials:
      PUB: https://www.mdpi.com/2227-7390/10/6/1000
  - title: Federated Dynamic Graph Neural Networks with Secure Aggregation for Video-based
      Distributed Surveillance
    affiliation: ND
    venue: ACM Trans. Intell. Syst. Technol.
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3501808
      PDF: https://arxiv.org/abs/2009.07351
      解读: https://zhuanlan.zhihu.com/p/441686576
  - title: 'Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms,
      and Implementation.'
    affiliation: Purdue
    venue: 'INFOCOM :mortar_board:'
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488906
      PDF: https://arxiv.org/abs/2101.00787
  - title: Federated Graph Classification over Non-IID Graphs
    affiliation: Emory
    venue: 'NeurIPS :mortar_board:'
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html
      PDF: https://arxiv.org/abs/2106.13423
      CODE: https://github.com/Oxfordblue7/GCFL
      解读: https://zhuanlan.zhihu.com/p/430623053
  - title: Subgraph Federated Learning with Missing Neighbor Generation
    affiliation: Emory; UBC; Lehigh University
    venue: 'NeurIPS :mortar_board:'
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/34adeb8e3242824038aa65460a47c29e-Abstract.html
      PDF: https://arxiv.org/abs/2106.13430
  - title: Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
    affiliation: USC
    venue: 'KDD :mortar_board:'
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467371
      PDF: https://arxiv.org/abs/2106.05223
      CODE: https://github.com/mengcz13/KDD2021_CNFGNN
      解读: https://zhuanlan.zhihu.com/p/434839878
  - title: Differentially Private Federated Knowledge Graphs Embedding **`kg.`**
    affiliation: BUAA
    venue: CIKM
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3459637.3482252
      PDF: https://arxiv.org/abs/2105.07615
      CODE: https://github.com/HKUST-KnowComp/FKGE
      解读: https://zhuanlan.zhihu.com/p/437895959
  - title: Decentralized Federated Graph Neural Networks
    affiliation: Blue Elephant Tech
    venue: IJCAI Workshop
    year: '2021'
    materials:
      PDF: https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_20.pdf
  - title: 'FedSGC: Federated Simple Graph Convolution for Node Classification'
    affiliation: HKUST
    venue: IJCAI Workshop
    year: '2021'
    materials:
      PDF: https://federated-learning.org/fl-ijcai-2021/FTL-IJCAI21_paper_5.pdf
  - title: 'FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule
      Drug Discovery: Special Session Paper'
    affiliation: UNM
    venue: ICCAD
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/ICCAD51958.2021.9643440
  - title: 'FASTGNN: A Topological Information Protected Federated Learning Approach
      for Traffic Speed Forecasting'
    affiliation: UTS
    venue: IEEE Trans. Ind. Informatics
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9340313
  - title: 'DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated
      Learning'
    affiliation: BUPT; UESTC
    venue: ICC
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/ICC42927.2021.9500737
      PDF: https://arxiv.org/abs/2104.13092
  - title: 'FedE: Embedding Knowledge Graphs in Federated Setting **`kg.`**'
    affiliation: ZJU
    venue: IJCKG
    year: '2021'
    materials:
      PUB: https://doi.org/10.1145/3502223.3502233
      PDF: https://arxiv.org/abs/2010.12882
      CODE: https://github.com/AnselCmy/FedE
  - title: Federated Knowledge Graph Embeddings with Heterogeneous Data **`kg.`**
    affiliation: TJU
    venue: CCKS
    year: '2021'
    materials:
      PUB: https://doi.org/10.1007/978-981-16-6471-7_2
  - title: A Graph Federated Architecture with Privacy Preserving Learning
    affiliation: EPFL
    venue: SPAWC
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/SPAWC51858.2021.9593148
      PDF: https://arxiv.org/abs/2104.13215
      解读: https://zhuanlan.zhihu.com/p/440809332
  - title: Federated Social Recommendation with Graph Neural Network
    affiliation: UIC
    venue: ACM TIST
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/abs/10.1145/3501815
      PDF: https://arxiv.org/abs/2111.10778
      CODE: https://github.com/YangLiangwei/FeSoG
  - title: 'FedGraphNN: A Federated Learning System and Benchmark for Graph Neural
      Networks :fire: **`surv.`**'
    affiliation: USC
    venue: ICLR Workshop / MLSys Workshop
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2104.07145
      CODE: https://github.com/FedML-AI/FedGraphNN
      解读: https://zhuanlan.zhihu.com/p/429220636
  - title: A Federated Multigraph Integration Approach for Connectional Brain Template
      Learning
    affiliation: Istanbul Technical University
    venue: MICCAI Workshop
    year: '2021'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-89847-2_4
      CODE: https://github.com/basiralab/Fed-CBT
  - title: Cluster-driven Graph Federated Learning over Multiple Domains
    affiliation: Politecnico di Torino
    venue: CVPR Workshop
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2104.14628
      解读: https://zhuanlan.zhihu.com/p/440527314
  - title: 'FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation'
    affiliation: THU
    venue: ICML workshop
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2102.04925
      解读: https://zhuanlan.zhihu.com/p/428783383
  - title: Decentralized federated learning of deep neural networks on non-iid data
    affiliation: RISE; Chalmers University of Technology
    venue: ICML workshop
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2107.08517
      CODE: https://github.com/guskarls/dfl-pens
  - title: 'Glint: Decentralized Federated Graph Learning with Traffic Throttling
      and Flow Scheduling'
    affiliation: The University of Aizu
    venue: IWQoS
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/IWQOS52092.2021.9521331
  - title: Federated Graph Neural Network for Cross-graph Node Classification
    affiliation: BUPT
    venue: CCIS
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/CCIS53392.2021.9754598
  - title: 'GraFeHTy: Graph Neural Network using Federated Learning for Human Activity
      Recognition'
    affiliation: Lead Data Scientist Ericsson Digital Services
    venue: ICMLA
    year: '2021'
    materials:
      PUB: https://doi.org/10.1109/ICMLA52953.2021.00184
  - title: Distributed Training of Graph Convolutional Networks
    affiliation: Sapienza University of Rome
    venue: TSIPN
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9303371
      PDF: https://arxiv.org/abs/2007.06281
      解读: https://zhuanlan.zhihu.com/p/433329525
  - title: Decentralized federated learning for electronic health records
    affiliation: UMN
    venue: NeurIPS Workshop / CISS
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/abstract/document/9086196
      PDF: https://arxiv.org/abs/1912.01792
      解读: https://zhuanlan.zhihu.com/p/448738120
  - title: 'ASFGNN: Automated Separated-Federated Graph Neural Network'
    affiliation: Ant Group
    venue: PPNA
    year: '2020'
    materials:
      PUB: https://doi.org/10.1007/s12083-021-01074-w
      PDF: https://arxiv.org/abs/2011.03248
      解读: https://zhuanlan.zhihu.com/p/431283541
  - title: Decentralized federated learning via sgd over wireless d2d networks
    affiliation: SZU
    venue: SPAWC
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9154332
      PDF: https://arxiv.org/abs/2002.12507
  - title: 'SGNN: A Graph Neural Network Based Federated Learning Approach by Hiding
      Structure'
    affiliation: SDU
    venue: BigData
    year: '2019'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9005983
      PDF: https://www.researchgate.net/profile/Shijun_Liu3/publication/339482514_SGNN_A_Graph_Neural_Network_Based_Federated_Learning_Approach_by_Hiding_Structure/links/5f48365d458515a88b790595/SGNN-A-Graph-Neural-Network-Based-Federated-Learning-Approach-by-Hiding-Structure.pdf
  - title: Towards Federated Graph Learning for Collaborative Financial Crimes Detection
    affiliation: IBM
    venue: NeurIPS Workshop
    year: '2019'
    materials:
      PDF: https://arxiv.org/abs/1909.12946
  - title: 'Federated learning of predictive models from federated Electronic Health
      Records :star:'
    affiliation: BU
    venue: Int. J. Medical Informatics
    year: '2018'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S138650561830008X?via%3Dihub
  - title: 'FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks.'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2305.09729
      CODE: https://github.com/cynricfu/FedHGN
  - title: Graph-guided Personalization for Federated Recommendation.
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2305.07866
  - title: 'GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules
      Towards Efficient Drug Discovery.'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2304.05498
  - title: 'GLASU: A Communication-Efficient Algorithm for Federated Learning with
      Vertically Distributed Graph Data'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2303.09531
  - title: Vertical Federated Graph Neural Network for Recommender System
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2303.05786
      CODE: https://github.com/maiph123/verticalgnn
  - title: 'Lumos: Heterogeneity-aware Federated Graph Learning over Decentralized
      Devices'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2303.00492
  - title: Securing IoT Communication using Physical Sensor Data - Graph Layer Security  with
      Federated Multi-Agent Deep Reinforcement Learning.
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2302.12592
  - title: Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2302.02069
  - title: 'Uplink Scheduling in Federated Learning: an Importance-Aware Approach
      via Graph Representation Learning'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2301.11903
  - title: Graph Federated Learning with Hidden Representation Sharing
    affiliation: UCLA
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2212.12158
  - title: M3FGM:a node masking and multi-granularity message passing-based federated
      graph model for spatial-temporal data prediction
    affiliation: Xidian University
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2210.16193
  - title: Federated Graph-based Networks with Shared Embedding
    affiliation: BUCEA
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2210.01803
  - title: Privacy-preserving Decentralized Federated Learning over Time-varying Communication
      Graph
    affiliation: Lancaster University
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2210.00325
  - title: Heterogeneous Federated Learning on a Graph.
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2209.08737
  - title: 'FedEgo: Privacy-preserving Personalized Federated Graph Learning with
      Ego-graphs'
    affiliation: SYSU
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2208.13685
      CODE: https://github.com/fedego/fedego
  - title: Federated Graph Contrastive Learning
    affiliation: UTS
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2207.11836
  - title: 'FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for
      Doctor Recommendation Using EHR'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2207.05750
  - title: 'Privacy-preserving Graph Analytics: Secure Generation and Federated Learning'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2207.00048
  - title: Federated Graph Attention Network for Rumor Detection
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2206.05713
      CODE: https://github.com/baichuanzheng1/fedgat
  - title: 'FedRel: An Adaptive Federated Relevance Framework for Spatial Temporal
      Graph Learning'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2206.03420
  - title: Privatized Graph Federated Learning
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2203.07105
  - title: 'Federated Graph Neural Networks: Overview, Techniques and Challenges **`surv.`**'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2202.07256
  - title: Decentralized event-triggered federated learning with heterogeneous communication
      thresholds.
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://github.com/ShahryarBQ/EF_HC
  - title: Federated Learning with Heterogeneous Architectures using Graph HyperNetworks
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2201.08459
  - title: 'STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural
      Networks'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2111.06750
      CODE: https://github.com/jw9msjwjnpdrlfw/tsfl
  - title: 'PPSGCN: A Privacy-Preserving Subgraph Sampling Based Distributed GCN Training
      Method'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2110.12906
  - title: Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in
      Digital Libraries **`kg.`**
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2107.05447
  - title: Federated Myopic Community Detection with One-shot Communication
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2106.07255
  - title: Federated Graph Learning -- A Position Paper **`surv.`**
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2105.11099
  - title: A Vertical Federated Learning Framework for Graph Convolutional Network
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2106.11593
  - title: 'FedGL: Federated Graph Learning Framework with Global Self-Supervision'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2105.03170
  - title: 'FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional
      Network Search'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2104.04141
  - title: A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian
      Regularization
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2102.07148
      CODE: https://github.com/dual-grp/fedu_fmtl
  - title: Improving Federated Relational Data Modeling via Basis Alignment and Weight
      Penalty **`kg.`**
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2011.11369
  - title: 'GraphFederator: Federated Visual Analysis for Multi-party Graphs'
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2008.11989
  - title: Privacy-Preserving Graph Neural Network for Node Classification
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2005.11903
  - title: Peer-to-peer federated learning on graphs
    affiliation: UC
    venue: preprint
    year: '2019'
    materials:
      PDF: https://arxiv.org/abs/1901.11173
      解读: https://zhuanlan.zhihu.com/p/441944011
private-graph-neural-networks: {}
fl-on-tabular-data:
  header:
    title: 'Title                                                        '
    affiliation: ' Affiliation                        '
    venue: ' Venue                                                '
    year: ' Year '
    materials: ' Materials'
  length:
    title: 60
    affiliation: 34
    venue: 52
    year: 4
    materials: 60
  body:
  - title: 'SGBoost: An Efficient and Privacy-Preserving Vertical Federated Tree Boosting
      Framework'
    affiliation: Xidian University
    venue: 'IEEE Trans. Inf. Forensics Secur. :mortar_board:'
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10002374
      CODE: https://github.com/nds2022/SGBoost
  - title: Incentive-boosted Federated Crowdsourcing
    affiliation: SDU
    venue: 'AAAI :mortar_board:'
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.14439
  - title: Explaining predictions and attacks in federated learning via random forests
    affiliation: Universitat Rovira i Virgili
    venue: Appl. Intell.
    year: '2023'
    materials:
      PUB: https://link.springer.com/article/10.1007/s10489-022-03435-1
      CODE: https://github.com/RamiHaf/Explainable-Federated-Learning-via-Random-Forests
  - title: Boosting Accuracy of Differentially Private Federated Learning in Industrial
      IoT With Sparse Responses
    affiliation: ''
    venue: IEEE Trans. Ind. Informatics
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9743613
  - title: Driver Drowsiness EEG Detection Based on Tree Federated Learning and Interpretable
      Network.
    affiliation: ''
    venue: Int. J. Neural Syst.
    year: '2023'
    materials:
      PUB: https://www.worldscientific.com/doi/10.1142/S0129065723500090
  - title: 'FDPBoost: Federated differential privacy gradient boosting decision trees.'
    affiliation: ''
    venue: J. Inf. Secur. Appl.
    year: '2023'
    materials:
      PUB: https://www.sciencedirect.com/science/article/abs/pii/S2214212623000522?via%3Dihub
  - title: Gradient-less Federated Gradient Boosting Trees with Learnable Learning
      Rates.
    affiliation: ''
    venue: EuroMLSys
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3578356.3592579
      PDF: https://arxiv.org/abs/2304.07537
  - title: 'HT-Fed-GAN: Federated Generative Model for Decentralized Tabular Data
      Synthesis'
    affiliation: HIT
    venue: Entropy
    year: '2023'
    materials:
      PUB: https://www.mdpi.com/1099-4300/25/1/88
  - title: Blockchain-Based Swarm Learning for the Mitigation of Gradient Leakage
      in Federated Learning
    affiliation: University of Udine
    venue: IEEE Access
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10047894
  - title: 'OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving
      Desensitization'
    affiliation: ZJU
    venue: 'Proc. VLDB Endow. :mortar_board:'
    year: '2022'
    materials:
      PUB: https://www.vldb.org/pvldb/volumes/16/paper/OpBoost%3A%20A%20Vertical%20Federated%20Tree%20Boosting%20Framework%20Based%20on%20Order-Preserving%20Desensitization
      PDF: https://arxiv.org/abs/2210.01318
      CODE: https://github.com/alibaba-edu/mpc4j/tree/main/mpc4j-sml-opboost
  - title: 'RevFRF: Enabling Cross-Domain Random Forest Training With Revocable Federated
      Learning'
    affiliation: XIDIAN UNIVERSITY
    venue: 'IEEE Trans. Dependable Secur. Comput. :mortar_board:'
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9514457
      PDF: https://arxiv.org/abs/1911.03242
  - title: A Tree-based Model Averaging Approach for Personalized Treatment Effect
      Estimation from Heterogeneous Data Sources
    affiliation: University of Pittsburgh
    venue: 'ICML :mortar_board:'
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/tan22a.html
      PDF: https://arxiv.org/abs/2103.06261
      CODE: https://github.com/ellenxtan/ifedtree
  - title: Federated Boosted Decision Trees with Differential Privacy
    affiliation: University of Warwick
    venue: 'CCS :mortar_board:'
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3548606.3560687
      PDF: https://arxiv.org/abs/2210.02910
      CODE: https://github.com/Samuel-Maddock/federated-boosted-dp-trees
  - title: Federated Functional Gradient Boosting
    affiliation: University of Pennsylvania
    venue: 'AISTATS :mortar_board:'
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/shen22a.html
      PDF: https://arxiv.org/abs/2103.06972
      CODE: https://github.com/shenzebang/Federated-Learning-Pytorch
  - title: Tree-Based Models for Federated Learning Systems.
    affiliation: IBM Research
    venue: Federated Learning
    year: '2022'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-96896-0_2
  - title: 'Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case'
    affiliation: ''
    venue: ICCP
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10053975
  - title: 'Federated Learning for Tabular Data: Exploring Potential Risk to Privacy'
    affiliation: Newcastle University
    venue: ISSRE
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2210.06856
  - title: Federated Random Forests can improve local performance of predictive models
      for various healthcare applications
    affiliation: University of Marburg
    venue: Bioinform.
    year: '2022'
    materials:
      PUB: https://academic.oup.com/bioinformatics/article-abstract/38/8/2278/6525214
      CODE: https://featurecloud.ai/
  - title: 'FLForest: Byzantine-robust Federated Learning through Isolated Forest'
    affiliation: NUAA
    venue: ICPADS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10077947
  - title: 'Boosting the Federation: Cross-Silo Federated Learning without Gradient
      Descent.'
    affiliation: unito
    venue: IJCNN
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9892284
      CODE: https://github.com/ml-unito/federation_boosting
  - title: Federated Forest
    affiliation: JD
    venue: TBD
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9088965
      PDF: https://arxiv.org/abs/1905.10053
  - title: Sliding Focal Loss for Class Imbalance Classification in Federated XGBoost.
    affiliation: Swinburne University of Technology
    venue: ISPA/BDCloud/SocialCom/SustainCom
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10070688
  - title: 'Neural gradient boosting in federated learning for hemodynamic instability  prediction:
      towards a distributed and scalable deep learning-based  solution.'
    affiliation: ''
    venue: AMIA
    year: '2022'
    materials:
      PUB: https://knowledge.amia.org/76677-amia-1.4637602/f006-1.4642154/f006-1.4642155/917-1.4642324/604-1.4642321?qr=1
  - title: 'Fed-GBM: a cost-effective federated gradient boosting tree for non-intrusive
      load monitoring'
    affiliation: The University of Sydney
    venue: e-Energy
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3538637.3538840
  - title: Verifiable Privacy-Preserving Scheme Based on Vertical Federated Random
      Forest
    affiliation: NUST
    venue: IEEE Internet Things J.
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9461157
  - title: Statistical Detection of Adversarial examples in Blockchain-based Federated
      Forest In-vehicle Network Intrusion Detection Systems
    affiliation: CNU
    venue: IEEE Access
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9912427
      PDF: https://arxiv.org/abs/2207.04843
  - title: 'BOFRF: A Novel Boosting-Based Federated Random Forest Algorithm on Horizontally
      Partitioned Data'
    affiliation: METU
    venue: IEEE Access
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9867984/
  - title: 'eFL-Boost: Efficient Federated Learning for Gradient Boosting Decision
      Trees'
    affiliation: kobe-u
    venue: IEEE Access
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9761890
  - title: An Efficient Learning Framework for Federated XGBoost Using Secret Sharing
      and Distributed Optimization
    affiliation: TJU
    venue: ACM Trans. Intell. Syst. Technol.
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3523061
      PDF: https://arxiv.org/abs/2105.05717
      CODE: https://github.com/HikariX/MP-FedXGB
  - title: An optional splitting extraction based gain-AUPRC balanced strategy in  federated
      XGBoost for mitigating imbalanced credit card fraud detection
    affiliation: Swinburne University of Technology
    venue: Int. J. Bio Inspired Comput.
    year: '2022'
    materials:
      PUB: https://www.inderscience.com/offer.php?id=126793
  - title: Random Forest Based on Federated Learning for Intrusion Detection
    affiliation: Malardalen University
    venue: AIAI
    year: '2022'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-031-08333-4_11
  - title: Cross-silo federated learning based decision trees
    affiliation: ETH Zürich
    venue: SAC
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477314.3507149
  - title: Leveraging Spanning Tree to Detect Colluding Attackers in Federated Learning
    affiliation: Missouri S&T
    venue: INFCOM Workshops
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9798077
  - title: 'VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise
      Learning'
    affiliation: PKU
    venue: 'SIGMOD :mortar_board:'
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3448016.3457241
  - title: Boosting with Multiple Sources
    affiliation: Google
    venue: 'NeurIPS:mortar_board:'
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=1oP1duoZxx
  - title: 'SecureBoost: A Lossless Federated Learning Framework :fire:'
    affiliation: UC
    venue: IEEE Intell. Syst.
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9440789/
      PDF: https://arxiv.org/abs/1901.08755
      SLIDE: https://fate.readthedocs.io/en/latest/resources/SecureBoost-ijcai2019-workshop.pdf
      CODE: https://github.com/FederatedAI/FATE/tree/master/python/federatedml/ensemble/secureboost
      解读: https://zhuanlan.zhihu.com/p/545739311
      UC: https://github.com/Koukyosyumei/AIJack
  - title: A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network
      Intrusion Detection System
    affiliation: CNU
    venue: IEEE Access
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9471858
  - title: Research on privacy protection of multi source data based on improved gbdt
      federated ensemble method with different metrics
    affiliation: NCUT
    venue: Phys. Commun.
    year: '2021'
    materials:
      PUB: https://www.sciencedirect.com/science/article/pii/S1874490721000847
  - title: 'Fed-EINI: An Efficient and Interpretable Inference Framework for Decision
      Tree Ensembles in Vertical Federated Learning'
    affiliation: UCAS; CAS
    venue: IEEE BigData
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9671749
      PDF: https://arxiv.org/abs/2105.09540
  - title: 'Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated
      Learning of GBDTs'
    affiliation: THU
    venue: ICONIP
    year: '2021'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-92270-2_7
  - title: A k-Anonymised Federated Learning Framework with Decision Trees
    affiliation: Umeå University
    venue: DPM/CBT @ESORICS
    year: '2021'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-93944-1_7
  - title: 'AF-DNDF: Asynchronous Federated Learning of Deep Neural Decision Forests'
    affiliation: Chalmers
    venue: SEAA
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9582575
  - title: Compression Boosts Differentially Private Federated Learning
    affiliation: Univ. Grenoble Alpes
    venue: EuroS&P
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9581200
      PDF: https://arxiv.org/abs/2011.05578
  - title: Practical Federated Gradient Boosting Decision Trees
    affiliation: NUS; UWA
    venue: 'AAAI :mortar_board:'
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/5895
      PDF: https://arxiv.org/abs/1911.04206
      CODE: https://github.com/Xtra-Computing/SimFL
  - title: Privacy Preserving Vertical Federated Learning for Tree-based Models
    affiliation: NUS
    venue: 'VLDB :mortar_board:'
    year: '2020'
    materials:
      PUB: http://vldb.org/pvldb/vol13/p2090-wu.pdf
      PDF: https://arxiv.org/abs/2008.06170
      VIDEO: https://www.youtube.com/watch?v=sjii8oVCqiY
      CODE: https://github.com/nusdbsystem/pivot
  - title: 'Boosting Privately: Federated Extreme Gradient Boosting for Mobile Crowdsensing'
    affiliation: Xidian University
    venue: ICDCS
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9355600
      PDF: https://arxiv.org/abs/1907.10218
  - title: 'FedCluster: Boosting the Convergence of Federated Learning via Cluster-Cycling'
    affiliation: University of Utah
    venue: IEEE BigData
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9377960
      PDF: https://arxiv.org/abs/2009.10748
  - title: New Approaches to Federated XGBoost Learning for Privacy-Preserving Data
      Analysis
    affiliation: kobe-u
    venue: ICONIP
    year: '2020'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-63833-7_47
  - title: Bandwidth Slicing to Boost Federated Learning Over Passive Optical Networks
    affiliation: Chalmers University of Technology
    venue: IEEE Communications Letters
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9044640
  - title: 'DFedForest: Decentralized Federated Forest'
    affiliation: UFRJ
    venue: Blockchain
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9284805/
  - title: Straggler Remission for Federated Learning via Decentralized Redundant
      Cayley Tree
    affiliation: Stevens Institute of Technology
    venue: LATINCOM
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9282334
  - title: Federated Soft Gradient Boosting Machine for Streaming Data
    affiliation: Sinovation Ventures AI Institute
    venue: Federated Learning
    year: '2020'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-63076-8_7
      解读: https://www.leiphone.com/category/academic/4tVdYDuYTA293NCy.html
  - title: Federated Learning of Deep Neural Decision Forests
    affiliation: Fraunhofer-Chalmers Centre
    venue: LOD
    year: '2019'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-37599-7_58
  - title: 'Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient
      Boosted Decision Tables.'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2305.12652
  - title: V2X-Boosted Federated Learning for Cooperative Intelligent Transportation
      Systems with Contextual Client Selection.
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2305.11654
  - title: 'GTV: Generating Tabular Data via Vertical Federated Learning'
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2302.01706
  - title: Federated Survival Forests
    affiliation: ''
    venue: preprint
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2302.02807
  - title: 'Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data'
    affiliation: HIT
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2211.13116
  - title: Data Leakage in Tabular Federated Learning
    affiliation: ETH Zurich
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2210.01785
  - title: Boost Decentralized Federated Learning in Vehicular Networks by Diversifying
      Data Sources
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2209.01750
  - title: Federated XGBoost on Sample-Wise Non-IID Data
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2209.01340
  - title: 'Hercules: Boosting the Performance of Privacy-preserving Federated Learning'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2207.04620
  - title: 'FedGBF: An efficient vertical federated learning framework via gradient
      boosting and bagging'
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2204.00976
  - title: A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost
      for Distributed Power Prediction.
    affiliation: THU
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2201.02783
  - title: An Efficient and Robust System for Vertically Federated Random Forest
    affiliation: ''
    venue: preprint
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2201.10761
  - title: Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost.
    affiliation: BUAA
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2112.04261
  - title: Guess what? You can boost Federated Learning for free
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2110.11486
  - title: 'SecureBoost+ : A High Performance Gradient Boosting Tree Framework for
      Large Scale Vertical Federated Learning :fire:'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2110.10927
      CODE: https://github.com/FederatedAI/FATE
  - title: 'Fed-TGAN: Federated Learning Framework for Synthesizing Tabular Data'
    affiliation: ''
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2108.07927
  - title: 'FedXGBoost: Privacy-Preserving XGBoost for Federated Learning'
    affiliation: TUM
    venue: preprint
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2106.10662
  - title: Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2012.06670
  - title: 'FederBoost: Private Federated Learning for GBDT'
    affiliation: ZJU
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2011.02796
  - title: Privacy Preserving Text Recognition with Gradient-Boosting for Federated
      Learning
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2007.07296
      CODE: https://github.com/rand2ai/fedboost
  - title: Cloud-based Federated Boosting for Mobile Crowdsensing
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      ARXIV: https://arxiv.org/abs/2005.05304
  - title: Federated Extra-Trees with Privacy Preserving
    affiliation: ''
    venue: preprint
    year: '2020'
    materials:
      PDF: https://arxiv.org/abs/2002.07323.pdf
  - title: Bandwidth Slicing to Boost Federated Learning in Edge Computing
    affiliation: ''
    venue: preprint
    year: '2019'
    materials:
      PDF: https://arxiv.org/abs/1911.07615
  - title: 'Revocable Federated Learning: A Benchmark of Federated Forest'
    affiliation: ''
    venue: preprint
    year: '2019'
    materials:
      PDF: https://arxiv.org/abs/1911.03242
  - title: The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated
      XGBoost
    affiliation: CUHK
    venue: preprint
    year: '2019'
    materials:
      PDF: https://arxiv.org/abs/1907.07157
      CODE: https://github.com/Raymw/Federated-XGBoost
fl-in-top-tier-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation    '
    venue: '    Venue                    '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 11
    venue: 21
    year: 4
    materials: 60
  body:
  - title: Federated machine learning in data-protection-compliant research
    affiliation: University of Hamburg
    venue: Nat. Mach. Intell.(Comment)
    year: '2023'
    materials:
      PUB: https://www.nature.com/articles/s42256-022-00601-5
  - title: Federated learning for predicting histological response to neoadjuvant
      chemotherapy in triple-negative breast cancer
    affiliation: Owkin
    venue: Nat. Med.
    year: '2023'
    materials:
      PUB: https://www.nature.com/articles/s41591-022-02155-w
      CODE: https://github.com/Substra/substra
  - title: Federated learning enables big data for rare cancer boundary detection
    affiliation: University of Pennsylvania
    venue: Nat. Commun.
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41467-022-33407-5
      PDF: https://arxiv.org/abs/2204.10836
      CODE: https://github.com/FETS-AI/Front-End
  - title: Federated learning and Indigenous genomic data sovereignty
    affiliation: Hugging Face
    venue: Nat. Mach. Intell. (Comment)
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s42256-022-00551-y
  - title: Federated disentangled representation learning for unsupervised brain anomaly
      detection
    affiliation: TUM
    venue: Nat. Mach. Intell.
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s42256-022-00515-2
      PDF: https://doi.org/https://doi.org/10.21203/rs.3.rs-722389/v1
      CODE: https://doi.org/10.5281/zenodo.6604161
  - title: Shifting machine learning for healthcare from development to deployment
      and from models to data
    affiliation: ''
    venue: Nat. Biomed. Eng. (Review Article)
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41551-022-00898-y
  - title: A federated graph neural network framework for privacy-preserving personalization
    affiliation: THU
    venue: Nat. Commun.
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41467-022-30714-9
      CODE: https://github.com/wuch15/FedPerGNN
      解读: https://zhuanlan.zhihu.com/p/487383715
  - title: Communication-efficient federated learning via knowledge distillation
    affiliation: ''
    venue: Nat. Commun.
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41467-022-29763-x
      PDF: https://arxiv.org/abs/2108.13323
      CODE: https://zenodo.org/record/6383473
  - title: Lead federated neuromorphic learning for wireless edge artificial intelligence
    affiliation: ''
    venue: Nat. Commun.
    year: '2022'
    materials:
      PUB: https://www.nature.com/articles/s41467-022-32020-w
      CODE: https://github.com/GOGODD/FL-EDGE-COMPUTING/releases/tag/federated_learning
      解读: https://zhuanlan.zhihu.com/p/549087420
  - title: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial
      intelligence
    affiliation: ''
    venue: Nat. Mach. Intell.
    year: '2021'
    materials:
      PUB: https://www.nature.com/articles/s42256-021-00421-z
      PDF: https://arxiv.org/abs/2111.09461
      CODE: https://github.com/HUST-EIC-AI-LAB/UCADI
  - title: Federated learning for predicting clinical outcomes in patients with COVID-19
    affiliation: ''
    venue: Nat. Med.
    year: '2021'
    materials:
      PUB: https://www.nature.com/articles/s41591-021-01506-3
      CODE: https://www.nature.com/articles/s41591-021-01506-3#code-availability
  - title: Adversarial interference and its mitigations in privacy-preserving collaborative
      machine learning
    affiliation: ''
    venue: Nat. Mach. Intell.(Perspective)
    year: '2021'
    materials:
      PUB: https://www.nature.com/articles/s42256-021-00390-3
  - title: 'Swarm Learning for decentralized and confidential clinical machine learning
      :star:'
    affiliation: ''
    venue: 'Nature :mortar_board:'
    year: '2021'
    materials:
      PUB: https://www.nature.com/articles/s41586-021-03583-3
      CODE: https://github.com/HewlettPackard/swarm-learning
      SOFTWARE: https://myenterpriselicense.hpe.com
      解读: https://zhuanlan.zhihu.com/p/379434722
  - title: End-to-end privacy preserving deep learning on multi-institutional medical
      imaging
    affiliation: ''
    venue: Nat. Mach. Intell.
    year: '2021'
    materials:
      PUB: https://www.nature.com/articles/s42256-021-00337-8
      CODE: https://doi.org/10.5281/zenodo.4545599
      解读: https://zhuanlan.zhihu.com/p/484801505
  - title: Communication-efficient federated learning
    affiliation: ''
    venue: PANS.
    year: '2021'
    materials:
      PUB: https://www.pnas.org/doi/full/10.1073/pnas.2024789118
      CODE: https://code.ihub.org.cn/projects/4394/repository/revisions/master/show/PNAS
  - title: Breaking medical data sharing boundaries by using synthesized radiographs
    affiliation: ''
    venue: Science. Advances.
    year: '2020'
    materials:
      PUB: https://www.science.org/doi/10.1126/sciadv.abb7973
      CODE: https://github.com/peterhan91/Thorax_GAN
  - title: 'Secure, privacy-preserving and federated machine learning in medical imaging
      :star:'
    affiliation: ''
    venue: Nat. Mach. Intell.(Perspective)
    year: '2020'
    materials:
      PUB: https://www.nature.com/articles/s42256-020-0186-1
fl-in-top-ai-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                     '
    venue: '    Venue      '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 60
    venue: 7
    year: 4
    materials: 60
  body:
  - title: Federated Learning on Non-IID Graphs via Structural Knowledge Sharing
    affiliation: UTS
    venue: AAAI
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.13009
      CODE: https://github.com/yuetan031/fedstar
  - title: 'FedGS: Federated Graph-based Sampling with Arbitrary Client Availability'
    affiliation: XMU
    venue: AAAI
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.13975
      CODE: https://github.com/wwzzz/fedgs
  - title: Incentive-boosted Federated Crowdsourcing
    affiliation: SDU
    venue: AAAI
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2211.14439
  - title: Towards Understanding Biased Client Selection in Federated Learning.
    affiliation: CMU
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/jee-cho22a.html
      CODE: https://proceedings.mlr.press/v151/jee-cho22a/jee-cho22a-supp.zip
  - title: 'FLIX: A Simple and Communication-Efficient Alternative to Local Methods
      in Federated Learning'
    affiliation: KAUST
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/gasanov22a.html
      PDF: https://arxiv.org/abs/2111.11556
      CODE: https://proceedings.mlr.press/v151/gasanov22a/gasanov22a-supp.zip
  - title: Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective.
    affiliation: Stanford
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/glasgow22a.html
      PDF: https://arxiv.org/abs/2111.03741
      CODE: https://github.com/hongliny/sharp-bounds-for-fedavg-and-continuous-perspective
  - title: Federated Reinforcement Learning with Environment Heterogeneity.
    affiliation: PKU
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/jin22a.html
      PDF: https://arxiv.org/abs/2204.02634
      CODE: https://github.com/pengyang7881187/fedrl
  - title: Federated Myopic Community Detection with One-shot Communication
    affiliation: Purdue
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/ke22a.html
      PDF: https://arxiv.org/abs/2106.07255
  - title: Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits.
    affiliation: University of Virginia
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/li22e.html
      PDF: https://arxiv.org/abs/2110.01463
      CODE: https://github.com/cyrilli/Async-LinUCB
  - title: Towards Federated Bayesian Network Structure Learning with Continuous Optimization.
    affiliation: CMU
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/ng22a.html
      PDF: https://arxiv.org/abs/2110.09356
      CODE: https://github.com/ignavierng/notears-admm
  - title: Federated Learning with Buffered Asynchronous Aggregation
    affiliation: Meta AI
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/nguyen22b.html
      PDF: https://arxiv.org/abs/2106.06639
      VIDEO: https://www.youtube.com/watch?v=Ui-OGUAieNY&ab_channel=FederatedLearningOneWorldSeminar
  - title: Differentially Private Federated Learning on Heterogeneous Data.
    affiliation: Stanford
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/noble22a.html
      PDF: https://arxiv.org/abs/2111.09278
      CODE: https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning
  - title: 'SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with
      Sparsification'
    affiliation: Princeton
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/panda22a.html
      PDF: https://arxiv.org/abs/2112.06274
      CODE: https://github.com/sparsefed/sparsefed
      VIDEO: https://www.youtube.com/watch?v=TXG7ZScheas&ab_channel=GoogleTechTalks
  - title: 'Basis Matters: Better Communication-Efficient Second Order Methods for
      Federated Learning'
    affiliation: KAUST
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/qian22a.html
      PDF: https://arxiv.org/abs/2111.01847
  - title: Federated Functional Gradient Boosting.
    affiliation: University of Pennsylvania
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/shen22a.html
      PDF: https://arxiv.org/abs/2103.06972
      CODE: https://github.com/shenzebang/Federated-Learning-Pytorch
  - title: 'QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning.'
    affiliation: Criteo AI Lab
    venue: AISTATS
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v151/vono22a.html
      PDF: https://arxiv.org/abs/2106.00797
      CODE: https://proceedings.mlr.press/v151/vono22a/vono22a-supp.zip
      VIDEO: https://www.youtube.com/watch?v=fY8V184It1g&ab_channel=FederatedLearningOneWorldSeminar
  - title: Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the
      Federated Setting **`kg.`**
    affiliation: ZJU
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/273
      PDF: https://doi.org/10.48550/arXiv.2205.04692
      CODE: https://github.com/zjukg/maker
  - title: Personalized Federated Learning With a Graph
    affiliation: UTS
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/357
      PDF: https://arxiv.org/abs/2203.00829
      CODE: https://github.com/dawenzi098/SFL-Structural-Federated-Learning
  - title: Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
    affiliation: ZJU
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/272
      PDF: https://arxiv.org/abs/2005.11903
  - title: 'Adapt to Adaptation: Learning Personalization for Cross-Silo Federated
      Learning'
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/301
      PDF: https://arxiv.org/abs/2110.08394
      CODE: https://github.com/ljaiverson/pFL-APPLE
  - title: Heterogeneous Ensemble Knowledge Transfer for Training Large Models in
      Federated Learning
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/399
      PDF: https://arxiv.org/abs/2204.12703
  - title: Private Semi-Supervised Federated Learning.
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/279
  - title: Continual Federated Learning Based on Knowledge Distillation.
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://doi.org/10.24963/ijcai.2022/306
  - title: Federated Learning on Heterogeneous and Long-Tailed Data via Classifier
      Re-Training with Federated Features
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/308
      PDF: https://arxiv.org/abs/2204.13399
      CODE: https://github.com/shangxinyi/CReFF-FL
  - title: Federated Multi-Task Attention for Cross-Individual Human Activity Recognition
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/475
  - title: Personalized Federated Learning with Contextualized Generalization.
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/311
      PDF: https://arxiv.org/abs/2106.13044
  - title: 'Shielding Federated Learning: Robust Aggregation with Adaptive Client
      Selection.'
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/106
      PDF: https://arxiv.org/abs/2204.13256
  - title: 'FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining
      Competitive Performance in Federated Learning'
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/324
      PDF: https://arxiv.org/abs/2111.08211
      CODE: https://github.com/FederatedAI/research/tree/main/publications/FedCG
  - title: 'FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using
      Shared Data on the Server.'
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/385
      PDF: https://arxiv.org/abs/2204.11536
  - title: Towards Verifiable Federated Learning **`surv.`**
    affiliation: ''
    venue: IJCAI
    year: '2022'
    materials:
      PUB: https://www.ijcai.org/proceedings/2022/792
      PDF: https://arxiv.org/abs/2202.08310
  - title: 'HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on
      Heterogeneous Medical Images'
    affiliation: CUHK; BUAA
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/19993
      PDF: https://arxiv.org/abs/2112.10775
      CODE: https://github.com/med-air/HarmoFL
      解读: https://zhuanlan.zhihu.com/p/472555067
  - title: Federated Learning for Face Recognition with Gradient Correction
    affiliation: BUPT
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20095
      PDF: https://arxiv.org/abs/2112.07246
  - title: 'SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural
      Networks on Molecular Data'
    affiliation: USC
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20643
      PDF: https://arxiv.org/abs/2106.02743
      CODE: https://github.com/FedML-AI/SpreadGNN
      解读: https://zhuanlan.zhihu.com/p/429720860
  - title: 'SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting
      the CNNs Structures'
    affiliation: HIT; PCL
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20345
      CODE: https://github.com/wudonglei99/smartidx
  - title: Bridging between Cognitive Processing Signals and Linguistic Features via
      a Unified Attentional Network
    affiliation: TJU
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/19878
      PDF: https://arxiv.org/abs/2112.08831
  - title: Seizing Critical Learning Periods in Federated Learning
    affiliation: SUNY-Binghamton University
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20859
      PDF: https://arxiv.org/abs/2109.05613
  - title: Coordinating Momenta for Cross-silo Federated Learning
    affiliation: University of Pittsburgh
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20853
      PDF: https://arxiv.org/abs/2102.03970
  - title: 'FedProto: Federated Prototype Learning over Heterogeneous Devices'
    affiliation: UTS
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20819
      PDF: https://arxiv.org/abs/2105.00243
      CODE: https://github.com/yuetan031/fedproto
  - title: 'FedSoft: Soft Clustered Federated Learning with Proximal Local Updating'
    affiliation: CMU
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20785
      PDF: https://arxiv.org/abs/2112.06053
      CODE: https://github.com/ycruan/FedSoft
  - title: 'Federated Dynamic Sparse Training: Computing Less, Communicating Less,
      Yet Learning Better'
    affiliation: The University of Texas at Austin
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20555
      PDF: https://arxiv.org/abs/2112.09824
      CODE: https://github.com/bibikar/feddst
  - title: 'FedFR: Joint Optimization Federated Framework for Generic and Personalized
      Face Recognition'
    affiliation: National Taiwan University
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20057
      PDF: https://arxiv.org/abs/2112.12496
      CODE: https://github.com/jackie840129/fedfr
  - title: 'SplitFed: When Federated Learning Meets Split Learning'
    affiliation: CSIRO
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20825
      PDF: https://arxiv.org/abs/2004.12088
      CODE: https://github.com/chandra2thapa/SplitFed-When-Federated-Learning-Meets-Split-Learning
  - title: Efficient Device Scheduling with Multi-Job Federated Learning
    affiliation: Soochow University
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/21235
      PDF: https://arxiv.org/abs/2112.05928
  - title: Implicit Gradient Alignment in Distributed and Federated Learning
    affiliation: IIT Kanpur
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20597
      PDF: https://arxiv.org/abs/2106.13897
  - title: Federated Nearest Neighbor Classification with a Colony of Fruit-Flies
    affiliation: IBM Research
    venue: AAAI
    year: '2022'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/20775
      PDF: https://arxiv.org/abs/2112.07157
      CODE: https://github.com/rithram/flynn
  - title: Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/202
      PDF: https://arxiv.org/abs/2008.01558
      VIDEO: https://papertalk.org/papertalks/35198
  - title: 'Behavior Mimics Distribution: Combining Individual and Group Behaviors
      for Federated Learning'
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/352
      PDF: https://arxiv.org/abs/2106.12300
  - title: 'FedSpeech: Federated Text-to-Speech with Continual Learning'
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/527
      PDF: https://arxiv.org/abs/2110.07216
  - title: Practical One-Shot Federated Learning for Cross-Silo Setting
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/205
      PDF: https://arxiv.org/abs/2010.01017
      CODE: https://github.com/QinbinLi/FedKT
  - title: Federated Model Distillation with Noise-Free Differential Privacy
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/216
      PDF: https://arxiv.org/abs/2202.08310
      VIDEO: https://papertalk.org/papertalks/35184
  - title: 'LDP-FL: Practical Private Aggregation in Federated Learning with Local
      Differential Privacy'
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/217
      PDF: https://arxiv.org/abs/2007.15789
  - title: 'Federated Learning with Fair Averaging. :fire:'
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/223
      PDF: https://arxiv.org/abs/2104.14937
      CODE: https://github.com/WwZzz/easyFL
  - title: 'H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture
      for Federated Learning.'
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/67
      PDF: https://arxiv.org/abs/2106.00275
  - title: Communication-efficient and Scalable Decentralized Federated Edge Learning.
    affiliation: ''
    venue: IJCAI
    year: '2021'
    materials:
      PUB: https://www.ijcai.org/proceedings/2021/720
  - title: Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating
    affiliation: Xidian University; JD Tech
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17301
      PDF: https://arxiv.org/abs/2103.00958
      VIDEO: https://slideslive.com/38947765/secure-bilevel-asynchronous-vertical-federated-learning-with-backward-updating
  - title: 'FedRec++: Lossless Federated Recommendation with Explicit Feedback'
    affiliation: SZU
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/16546
      VIDEO: https://slideslive.com/38947798/fedrec-lossless-federated-recommendation-with-explicit-feedback
  - title: Federated Multi-Armed Bandits
    affiliation: University of Virginia
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17156
      PDF: https://arxiv.org/abs/2101.12204
      CODE: https://github.com/ShenGroup/FMAB
      VIDEO: https://slideslive.com/38947985/federated-multiarmed-bandits
  - title: On the Convergence of Communication-Efficient Local SGD for Federated Learning
    affiliation: Temple University; University of Pittsburgh
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/16920
      VIDEO: https://slideslive.com/38948341/on-the-convergence-of-communicationefficient-local-sgd-for-federated-learning
  - title: 'FLAME: Differentially Private Federated Learning in the Shuffle Model'
    affiliation: Renmin University of China; Kyoto University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17053
      PDF: https://arxiv.org/abs/2009.08063
      VIDEO: https://slideslive.com/38948496/flame-differentially-private-federated-learning-in-the-shuffle-model
      CODE: https://github.com/Rachelxuan11/FLAME
  - title: Toward Understanding the Influence of Individual Clients in Federated Learning
    affiliation: SJTU; The University of Texas at Dallas
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17263
      PDF: https://arxiv.org/abs/2012.10936
      VIDEO: https://slideslive.com/38948549/toward-understanding-the-influence-of-individual-clients-in-federated-learning
  - title: Provably Secure Federated Learning against Malicious Clients
    affiliation: Duke University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/16849
      PDF: https://arxiv.org/abs/2102.01854
      VIDEO: https://www.youtube.com/watch?v=LP4uqW18yA0&ab_channel=PurdueCERIAS
      SLIDE: https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf
  - title: Personalized Cross-Silo Federated Learning on Non-IID Data
    affiliation: Simon Fraser University; McMaster University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/16960
      PDF: https://arxiv.org/abs/2007.03797
      VIDEO: https://slideslive.com/38948676/personalized-crosssilo-federated-learning-on-noniid-data
      UC.: https://github.com/TsingZ0/PFL-Non-IID
  - title: 'Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation'
    affiliation: Cornell University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/16669
      PDF: https://arxiv.org/abs/2010.00753
      CODE: https://github.com/kpdonahue/model_sharing_games
      VIDEO: https://slideslive.com/38948684/modelsharing-games-analyzing-federated-learning-under-voluntary-participation
  - title: Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated
      Learning
    affiliation: University of Nevada; IBM Research
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17291
      PDF: https://arxiv.org/abs/2102.00655
      VIDEO: https://slideslive.com/38949098/curse-or-redemption-how-data-heterogeneity-affects-the-robustness-of-federated-learning
  - title: 'Game of Gradients: Mitigating Irrelevant Clients in Federated Learning'
    affiliation: IIT Bombay; IBM Research
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17093
      PDF: https://arxiv.org/abs/2110.12257
      CODE: https://github.com/nlokeshiisc/sfedavg-aaai21
      VIDEO: https://slideslive.com/38949109/game-of-gradients-mitigating-irrelevant-clients-in-federated-learning
      SUPP: https://github.com/nlokeshiisc/SFedAvg-AAAI21
  - title: Federated Block Coordinate Descent Scheme for Learning Global and Personalized
      Models
    affiliation: CUHK; Arizona State University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17240
      PDF: https://arxiv.org/abs/2012.13900
      VIDEO: https://slideslive.com/38949195/federated-block-coordinate-descent-scheme-for-learning-global-and-personalized-models
      CODE: https://github.com/REIYANG/FedBCD
  - title: Addressing Class Imbalance in Federated Learning
    affiliation: Northwestern University
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17219
      PDF: https://arxiv.org/abs/2008.06217
      VIDEO: https://slideslive.com/38949283/adressing-class-imbalance-in-federated-learning
      CODE: https://github.com/balanced-fl/Addressing-Class-Imbalance-FL
      解读: https://zhuanlan.zhihu.com/p/443009189
  - title: Defending against Backdoors in Federated Learning with Robust Learning
      Rate
    affiliation: The University of Texas at Dallas
    venue: AAAI
    year: '2021'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/17118
      PDF: https://arxiv.org/abs/2007.03767
      VIDEO: https://slideslive.com/38949344/defending-against-backdoors-in-federated-learning-with-robust-learning-rate
      CODE: https://github.com/TinfoilHat0/Defending-Against-Backdoors-with-Robust-Learning-Rate
  - title: Free-rider Attacks on Model Aggregation in Federated Learning
    affiliation: Accenture Labs
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/fraboni21a.html
      PDF: https://arxiv.org/abs/2006.11901
      CODE: https://github.com/Accenture/Labs-Federated-Learning
      VIDEO: https://papertalk.org/papertalks/27640
      SUPP: http://proceedings.mlr.press/v130/fraboni21a/fraboni21a-supp.pdf
  - title: Federated f-differential privacy
    affiliation: University of Pennsylvania
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/zheng21a.html
      CODE: https://github.com/enosair/federated-fdp
      VIDEO: https://papertalk.org/papertalks/27595
      SUPP: http://proceedings.mlr.press/v130/zheng21a/zheng21a-supp.pdf
  - title: 'Federated learning with compression: Unified analysis and sharp guarantees
      :fire:'
    affiliation: The Pennsylvania State University; The University of Texas at Austin
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/haddadpour21a.html
      PDF: https://arxiv.org/abs/2007.01154
      CODE: https://github.com/MLOPTPSU/FedTorch
      VIDEO: https://papertalk.org/papertalks/27584
      SUPP: http://proceedings.mlr.press/v130/haddadpour21a/haddadpour21a-supp.pdf
  - title: Shuffled Model of Differential Privacy in Federated Learning
    affiliation: UCLA; Google
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/girgis21a.html
      VIDEO: https://papertalk.org/papertalks/27565
      SUPP: http://proceedings.mlr.press/v130/girgis21a/girgis21a-supp.pdf
  - title: Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning
    affiliation: Google
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/charles21a.html
      PDF: https://arxiv.org/abs/2103.05032
      VIDEO: https://papertalk.org/papertalks/27559
      SUPP: http://proceedings.mlr.press/v130/charles21a/charles21a-supp.pdf
  - title: Federated Multi-armed Bandits with Personalization
    affiliation: University of Virginia; The Pennsylvania State University
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/shi21c.html
      PDF: https://arxiv.org/abs/2102.13101
      CODE: https://github.com/ShenGroup/PF_MAB
      VIDEO: https://papertalk.org/papertalks/27521
      SUPP: http://proceedings.mlr.press/v130/shi21c/shi21c-supp.pdf
  - title: Towards Flexible Device Participation in Federated Learning
    affiliation: CMU; SYSU
    venue: AISTATS
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v130/ruan21a.html
      PDF: https://arxiv.org/abs/2006.06954
      VIDEO: https://papertalk.org/papertalks/27467
      SUPP: http://proceedings.mlr.press/v130/ruan21a/ruan21a-supp.pdf
  - title: Federated Meta-Learning for Fraudulent Credit Card Detection
    affiliation: ''
    venue: IJCAI
    year: '2020'
    materials:
      PUB: https://www.ijcai.org/proceedings/2020/642
      VIDEO: https://www.ijcai.org/proceedings/2020/video/23994
  - title: A Multi-player Game for Studying Federated Learning Incentive Schemes
    affiliation: ''
    venue: IJCAI
    year: '2020'
    materials:
      PUB: https://www.ijcai.org/proceedings/2020/769
      CODE: https://github.com/benggggggggg/fedgame
      解读: https://zhuanlan.zhihu.com/p/353868739
  - title: Practical Federated Gradient Boosting Decision Trees
    affiliation: NUS; UWA
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/5895
      PDF: https://arxiv.org/abs/1911.04206
      CODE: https://github.com/Xtra-Computing/PrivML
  - title: Federated Learning for Vision-and-Language Grounding Problems
    affiliation: PKU; Tencent
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/6824
  - title: 'Federated Latent Dirichlet Allocation: A Local Differential Privacy Based
      Framework'
    affiliation: BUAA
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/6096
  - title: Federated Patient Hashing
    affiliation: Cornell University
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/6121
  - title: Robust Federated Learning via Collaborative Machine Teaching
    affiliation: Symantec Research Labs; KAUST
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/5826
      PDF: https://arxiv.org/abs/1905.02941
  - title: 'FedVision: An Online Visual Object Detection Platform Powered by Federated
      Learning'
    affiliation: WeBank
    venue: AAAI
    year: '2020'
    materials:
      PUB: https://ojs.aaai.org/index.php/AAAI/article/view/7021
      PDF: https://arxiv.org/abs/2001.06202
      CODE: https://github.com/FederatedAI/FedVision
  - title: 'FedPAQ: A Communication-Efficient Federated Learning Method with Periodic
      Averaging and Quantization'
    affiliation: UC Santa Barbara; UT Austin
    venue: AISTATS
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v108/reisizadeh20a.html
      PDF: https://arxiv.org/abs/1909.13014
      VIDEO: https://papertalk.org/papertalks/7961
      SUPP: http://proceedings.mlr.press/v108/reisizadeh20a/reisizadeh20a-supp.pdf
  - title: 'How To Backdoor Federated Learning :fire:'
    affiliation: Cornell Tech
    venue: AISTATS
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v108/bagdasaryan20a.html
      PDF: https://arxiv.org/abs/1807.00459
      VIDEO: https://papertalk.org/papertalks/8046
      CODE: https://github.com/ebagdasa/backdoor_federated_learning
      SUPP: http://proceedings.mlr.press/v108/bagdasaryan20a/bagdasaryan20a-supp.pdf
  - title: Federated Heavy Hitters Discovery with Differential Privacy
    affiliation: RPI; Google
    venue: AISTATS
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v108/zhu20a.html
      PDF: https://arxiv.org/abs/1902.08534
      VIDEO: https://papertalk.org/papertalks/8129
      SUPP: http://proceedings.mlr.press/v108/zhu20a/zhu20a-supp.pdf
  - title: Multi-Agent Visualization for Explaining Federated Learning
    affiliation: WeBank
    venue: IJCAI
    year: '2019'
    materials:
      PUB: https://www.ijcai.org/proceedings/2019/960
      VIDEO: https://youtu.be/NPGf_OJrzOg
fl-in-top-ml-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                     '
    venue: '    Venue             '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 60
    venue: 14
    year: 4
    materials: 60
  body:
  - title: 'FedIPR: Ownership Verification for Federated Deep Neural Network Models'
    affiliation: SJTU
    venue: TPAMI
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9847383
      PDF: https://arxiv.org/abs/2109.13236
      CODE: https://github.com/purp1eHaze/FedIPR
      解读: https://zhuanlan.zhihu.com/p/562837170
  - title: Decentralized Federated Averaging
    affiliation: NUDT
    venue: TPAMI
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9850408
      PDF: https://arxiv.org/abs/2104.11375
  - title: Personalized Federated Learning with Feature Alignment and Classifier Collaboration
    affiliation: THU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=SXZr8aDKia
      CODE: https://github.com/JianXu95/FedPAC
  - title: 'MocoSFL: enabling cross-client collaborative self-supervised learning'
    affiliation: ASU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=2QGJXyMNoPz
      CODE: https://github.com/SonyAI/MocoSFL
  - title: Single-shot General Hyper-parameter Optimization for Federated Learning
    affiliation: IBM
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=3RhuF8foyPW
      PDF: https://arxiv.org/abs/2202.08338
      CODE: https://openreview.net/attachment?id=3RhuF8foyPW&name=SUPP_material
  - title: Where to Begin? Exploring the Impact of Pre-Training and Initialization
      in Federated
    affiliation: Facebook
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=Mpa3tRJFBb
      PDF: https://arxiv.org/abs/2206.15387
      CODE: https://github.com/facebookresearch/where_to_begin
  - title: 'FedExP: Speeding up Federated Averaging via Extrapolation'
    affiliation: CMU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=IPrzNbddXV
      PDF: https://arxiv.org/abs/2301.09604
      CODE: https://github.com/divyansh03/fedexp
  - title: Turning the Curse of Heterogeneity in Federated Learning into a Blessing
      for Out-of-Distribution Detection
    affiliation: MSU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=mMNimwRb7Gr
      CODE: https://github.com/illidanlab/FOSTER
  - title: 'DASHA: Distributed Nonconvex Optimization with Communication Compression
      and Optimal Oracle Complexity'
    affiliation: KAUST
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=VA1YpcNr7ul
      PDF: https://arxiv.org/abs/2202.01268
      CODE: https://github.com/mysteryresearcher/dasha
  - title: Machine Unlearning of Federated Clusters
    affiliation: University of Illinois
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=VzwfoFyYDga
      PDF: https://arxiv.org/abs/2210.16424
      CODE: https://openreview.net/attachment?id=VzwfoFyYDga&name=SUPP_material
  - title: Federated Neural Bandits
    affiliation: NUS
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=38m4h8HcNRL
      PDF: https://arxiv.org/abs/2205.14309
      CODE: https://openreview.net/attachment?id=38m4h8HcNRL&name=SUPP_material
  - title: 'FedFA:  Federated Feature Augmentation'
    affiliation: ETH Zurich
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=U9yFP90jU0
      PDF: https://arxiv.org/abs/2301.12995
      CODE: https://github.com/tfzhou/fedfa
  - title: 'Federated Learning as Variational Inference: A Scalable Expectation Propagation
      Approach'
    affiliation: CMU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=dZrQR7OR11
      PDF: https://arxiv.org/abs/2302.04228
      CODE: https://github.com/hanguo97/expectation-propagation
  - title: Better Generative Replay for Continual Federated Learning
    affiliation: University of Virginia
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=cRxYWKiTan
      CODE: https://github.com/daiqing98/FedCIL
  - title: Federated Learning from Small Datasets
    affiliation: IKIM
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=hDDV1lsRV8
      PDF: https://arxiv.org/abs/2110.03469
  - title: Federated Nearest Neighbor Machine Translation
    affiliation: USTC
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=R1U5G2spbLd
      PDF: https://arxiv.org/abs/2302.12211
  - title: Meta Knowledge Condensation for Federated Learning
    affiliation: A*STAR
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=TDf-XFAwc79
      PDF: https://arxiv.org/abs/2209.14851
  - title: Test-Time Robust Personalization for Federated Learning
    affiliation: EPFL
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=3aBuJEza5sq
      PDF: https://arxiv.org/abs/2205.10920
      CODE: https://openreview.net/attachment?id=3aBuJEza5sq&name=SUPP_material
  - title: 'DepthFL : Depthwise Federated Learning for Heterogeneous Clients'
    affiliation: SNU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=pf8RIZTMU58
  - title: Towards Addressing Label Skews in One-Shot Federated Learning
    affiliation: NUS
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=rzrqh85f4Sc
      CODE: https://openreview.net/attachment?id=rzrqh85f4Sc&name=SUPP_material
  - title: Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous
      Federated Learning
    affiliation: NUS
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=EXnIyMVTL8s
      PDF: https://arxiv.org/abs/2210.00226
      CODE: https://github.com/Yujun-Shi/FedCLS
  - title: 'Panning for Gold in Federated Learning: Targeted Text Extraction under
      Arbitrarily Large-Scale Aggregation'
    affiliation: UMD
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=A9WQaxYsfx
      CODE: https://openreview.net/attachment?id=A9WQaxYsfx&name=SUPP_material
  - title: 'SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication'
    affiliation: UMD
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=jh1nCir1R3d
      PDF: https://arxiv.org/abs/2210.14026
      CODE: https://openreview.net/attachment?id=jh1nCir1R3d&name=SUPP_material
  - title: 'Private Federated Learning Without a Trusted Server: Optimal Algorithms
      for Convex Losses'
    affiliation: USC
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=TVY6GoURrw
      PDF: https://arxiv.org/abs/2106.09779
      CODE: https://github.com/lowya/private-federated-learning-without-a-trusted-server
  - title: Effective passive membership inference attacks in federated learning against
      overparameterized models
    affiliation: Purdue University
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=QsCSLPP55Ku
  - title: 'FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and
      Federated Image Classification'
    affiliation: University of Cambridge
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=9aokcgBVIj1
      PDF: https://arxiv.org/abs/2206.08671
      CODE: https://openreview.net/attachment?id=9aokcgBVIj1&name=SUPP_material
  - title: Multimodal Federated Learning via Contrastive Representation Ensemble
    affiliation: THU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=Hnk1WRMAYqg
      PDF: https://arxiv.org/abs/2302.08888
      CODE: https://github.com/flair-thu/creamfl
  - title: Faster federated optimization under second-order similarity
    affiliation: Princeton University
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=ElC6LYO4MfD
      PDF: https://arxiv.org/abs/2209.02257
      CODE: https://openreview.net/attachment?id=ElC6LYO4MfD&name=SUPP_material
  - title: 'FedSpeed: Larger Local Interval, Less Communication Round, and Higher
      Generalization Accuracy'
    affiliation: University of Sydney
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=bZjxxYURKT
      CODE: https://openreview.net/attachment?id=bZjxxYURKT&name=SUPP_material
  - title: 'The Best of Both Worlds: Accurate  Global and Personalized Models through
      Federated Learning with Data-Free Hyper-Knowledge Distillation'
    affiliation: utexas
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=29V3AWjVAFi
      PDF: https://arxiv.org/abs/2301.08968
      CODE: https://openreview.net/attachment?id=29V3AWjVAFi&name=SUPP_material
  - title: 'PerFedMask: Personalized Federated Learning with Optimized Masking Vectors'
    affiliation: UBC
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=hxEIgUXLFF
      CODE: https://github.com/MehdiSet/PerFedMask
  - title: 'EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections
      for Federated Learning with Heterogeneous Data'
    affiliation: GMU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=ytZIYmztET
      CODE: https://github.com/MingruiLiu-ML-Lab/episode
  - title: 'FedDAR: Federated Domain-Aware Representation Learning'
    affiliation: Harvard
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=6P9Y25Pljl6
      PDF: https://arxiv.org/abs/2209.04007
      CODE: https://github.com/zlz0414/FedDAR
  - title: 'Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility
      Tradeoff in Federated Learning'
    affiliation: upenn
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=oJpVVGXu9i
      CODE: https://github.com/shenzebang/CENTAUR-Privacy-Federated-Representation-Learning
  - title: 'FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated
      Learning'
    affiliation: Purdue University
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=Xo2E217_M4n
      PDF: https://arxiv.org/abs/2210.12873
      CODE: https://github.com/KaiyuanZh/FLIP
  - title: 'Generalization Bounds for Federated Learning: Fast Rates, Unparticipating
      Clients and Unbounded Losses'
    affiliation: RUC
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=-EHqoysUYLx
  - title: Efficient Federated Domain Translation
    affiliation: Purdue University
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=uhLAcrAZ9cJ
      CODE: https://openreview.net/attachment?id=uhLAcrAZ9cJ&name=SUPP_material
  - title: On the Importance and Applicability of Pre-Training for Federated Learning
    affiliation: OSU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=fWWFv--P0xP
      PDF: https://arxiv.org/abs/2206.11488
      CODE: https://github.com/andytu28/fps_pre-training
  - title: 'Decepticons: Corrupted Transformers Breach Privacy in Federated Learning
      for Language Models'
    affiliation: UMD
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=r0BrY4BiEXO
      PDF: https://arxiv.org/abs/2201.12675
      CODE: https://github.com/JonasGeiping/breaching
  - title: 'A Statistical Framework for Personalized Federated Learning and Estimation:
      Theory, Algorithms, and Privacy'
    affiliation: UCLA
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=FUiDMCr_W4o
      PDF: https://arxiv.org/abs/2207.01771
  - title: Instance-wise Batch Label Restoration via Gradients in Federated Learning
    affiliation: BUAA
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=FIrQfNSOoTr
      CODE: https://github.com/BUAA-CST/iLRG
  - title: Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity
    affiliation: College of William and Mary
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=_hb4vM3jspB
  - title: 'CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated
      Learning'
    affiliation: University of Warwick
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=Kf7Yyf4O0u
      PDF: https://arxiv.org/abs/2210.02912
      CODE: https://github.com/facebookresearch/canife
  - title: Sparse Random Networks for Communication-Efficient Federated Learning
    affiliation: Stanford
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=k1FHgri5y3-
      PDF: https://arxiv.org/abs/2209.15328
      CODE: https://openreview.net/attachment?id=k1FHgri5y3-&name=SUPP_material
  - title: Combating Exacerbated Heterogeneity for Robust Decentralized Models
    affiliation: HKBU
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=eKllxpLOOm
      CODE: https://github.com/ZFancy/SFAT
  - title: Hyperparameter Optimization through Neural Network Partitioning
    affiliation: University of Cambridge
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=nAgdXgfmqj
      PDF: https://arxiv.org/abs/2304.14766
  - title: Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self
      Supervision?
    affiliation: MIT
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=2L9gzS80tA4
      PDF: https://arxiv.org/abs/2210.10947
      CODE: https://openreview.net/attachment?id=2L9gzS80tA4&name=SUPP_material
  - title: 'Variance Reduction is an Antidote  to Byzantines: Better Rates, Weaker
      Assumptions and Communication  Compression as a Cherry on the Top'
    affiliation: mbzuai
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=pfuqQQCB34
      PDF: https://arxiv.org/abs/2206.00529
      CODE: https://github.com/SamuelHorvath/VR_Byzantine
  - title: Dual Diffusion Implicit Bridges for Image-to-Image Translation
    affiliation: Stanford
    venue: ICLR
    year: '2023'
    materials:
      PUB: https://openreview.net/forum?id=5HLoTvVGDe
      PDF: https://arxiv.org/abs/2203.08382
      CODE: https://openreview.net/attachment?id=5HLoTvVGDe&name=SUPP_material
  - title: Federated online clustering of bandits.
    affiliation: CUHK
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=rKUgiU8iqeq
      PDF: https://arxiv.org/abs/2208.14865
      CODE: https://github.com/zhaohaoru/federated-clustering-of-bandits
  - title: Privacy-aware compression for federated data analysis.
    affiliation: Meta AI
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=BqUdRP8i9e9
      PDF: https://arxiv.org/abs/2203.08134
      CODE: https://github.com/facebookresearch/dp_compression
  - title: Faster non-convex federated learning via global and local momentum.
    affiliation: UTEXAS
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=SSlLRUIs9e9
      PDF: https://arxiv.org/abs/2012.04061
  - title: 'Fedvarp: Tackling the variance due to partial client participation in
      federated learning.'
    affiliation: CMU
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=HlWLLdUocx5
      PDF: https://arxiv.org/abs/2207.14130
  - title: 'SASH: Efficient secure aggregation based on SHPRG for federated learning'
    affiliation: CAS; CASTEST
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=HSleBPIoql9
      PDF: https://arxiv.org/abs/2111.12321
  - title: Bayesian federated estimation of causal effects from observational data
    affiliation: NUS
    venue: UAI
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=BEl3vP8sqlc
      PDF: https://arxiv.org/abs/2106.00456
  - title: Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated
      Learning
    affiliation: Hanyang University
    venue: TPAMI
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9625795
  - title: Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient
      Federated Learning
    affiliation: ZJU
    venue: TPAMI
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9238427
      CODE: https://github.com/sunjunaimer/TPAMI-LAQ
  - title: Communication Acceleration of Local Gradient Methods via an Accelerated
      Primal-Dual Algorithm with an Inexact Prox
    affiliation: Moscow Institute of Physics and Technology
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=W72rB0wwLVu
      PDF: https://arxiv.org/abs/2207.03957
  - title: 'LAMP: Extracting Text from Gradients with Language Model Priors'
    affiliation: ETHZ
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=6iqd9JAVR1z
      CODE: https://openreview.net/attachment?id=6iqd9JAVR1z&name=SUPP_material
  - title: 'FedAvg with Fine Tuning: Local Updates Lead to Representation Learning'
    affiliation: utexas
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=G3fswMh9P8y
      PDF: https://arxiv.org/abs/2205.13692
  - title: 'On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness
      and Beyond'
    affiliation: NUIST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=_33ynl9VgCX
      PDF: https://arxiv.org/abs/2206.05187
  - title: Improved Differential Privacy for SGD via Optimal Private Linear Operators
      on Adaptive Streams
    affiliation: WISC
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=i9XrHJoyLqJ
      CODE: https://openreview.net/attachment?id=i9XrHJoyLqJ&name=SUPP_material
  - title: Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication
      Networks
    affiliation: Columbia University
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Vj-jYs47cx
      PDF: https://arxiv.org/abs/2206.10870
  - title: 'Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion
      Perspective'
    affiliation: PKU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=wo-a8Ji6s3A
  - title: Subspace Recovery from Heterogeneous Data with Non-isotropic Noise
    affiliation: Stanford
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=mUeMOdJ2IJp
      PDF: https://arxiv.org/abs/2210.13497
  - title: 'EF-BV:  A Unified Theory of Error Feedback and Variance Reduction Mechanisms  for
      Biased and Unbiased Compression in Distributed Optimization'
    affiliation: KAUST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=PeJO709WUup
      PDF: https://arxiv.org/abs/2205.04180
  - title: 'On-Demand Sampling: Learning Optimally from Multiple Distributions'
    affiliation: UC Berkeley
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=FR289LMkmxZ
      CODE: https://openreview.net/attachment?id=FR289LMkmxZ&name=SUPP_material
  - title: Improved Utility Analysis of Private CountSketch
    affiliation: ITU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=XFCirHGr4Cs
      PDF: https://arxiv.org/abs/2205.08397
      CODE: https://github.com/rasmus-pagh/private-countsketch
  - title: Rate-Distortion Theoretic Bounds on Generalization Error for Distributed
      Learning
    affiliation: HUAWEI
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=APXedc0hgdT
      CODE: https://openreview.net/attachment?id=APXedc0hgdT&name=SUPP_material
  - title: Decentralized Local Stochastic Extra-Gradient for Variational Inequalities
    affiliation: phystech
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Y4vT7m4e3d
      PDF: https://arxiv.org/abs/2106.08315
  - title: 'BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication
      Compression'
    affiliation: Princeton
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=I47eFCKa1f3
      PDF: https://arxiv.org/abs/2201.13320
      CODE: https://github.com/liboyue/beer
  - title: Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for
      Communication Efficient Nonconvex Distributed Learning
    affiliation: The University of Tokyo
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=KOHC_CYEIuP
      PDF: https://arxiv.org/abs/2202.06083
  - title: Near-Optimal Collaborative Learning in Bandits
    affiliation: INRIA; Inserm
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=2xfJ26BuFP
      PDF: https://arxiv.org/abs/2206.00121
      CODE: https://github.com/clreda/near-optimal-federated
  - title: Distributed Methods with Compressed Communication for Solving Variational
      Inequalities, with Theoretical Guarantees
    affiliation: phystech
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=J0nhRuMkdGf
      PDF: https://arxiv.org/abs/2110.03313
  - title: Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
    affiliation: TTIC
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=SNElc7QmMDe
      CODE: https://openreview.net/attachment?id=SNElc7QmMDe&name=SUPP_material
  - title: 'FedPop: A Bayesian Approach for Personalised Federated Learning'
    affiliation: Skoltech
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=KETwimTQexH
      PDF: https://arxiv.org/abs/2206.03611
  - title: Fairness in Federated Learning via Core-Stability
    affiliation: UIUC
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=lKULHf7oFDo
      CODE: https://openreview.net/attachment?id=lKULHf7oFDo&name=SUPP_material
  - title: 'SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning'
    affiliation: Sorbonne Université
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=25XIE30VHZE
      PDF: https://arxiv.org/abs/2210.01639
  - title: 'FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel
      Extraction'
    affiliation: MSU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=OtxyysUdBE
      CODE: https://github.com/MSU-MLSys-Lab/FedRolex
  - title: On Sample Optimality in Personalized Collaborative and Federated Learning
    affiliation: INRIA
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=7EP90NMAoK
  - title: 'DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients
      via Secret Data Sharing'
    affiliation: HKUST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=hPkGV4BPsmv
      PDF: https://arxiv.org/abs/2210.02680
  - title: 'FairVFL: A Fair Vertical Federated Learning Framework with Contrastive
      Adversarial Learning'
    affiliation: THU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=5vVSA_cdRqe
  - title: 'Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated
      Learning'
    affiliation: KAUST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=edkno3SvKo
      PDF: https://arxiv.org/abs/2207.04338
  - title: 'VF-PS: How to Select Important Participants in Vertical Federated Learning,
      Efficiently and Securely?'
    affiliation: WHU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=vNrSXIFJ9wz
      CODE: https://openreview.net/attachment?id=edkno3SvKo&name=SUPP_material
  - title: 'DENSE: Data-Free One-Shot Federated Learning'
    affiliation: ZJU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=QFQoxCFYEkA
      PDF: https://arxiv.org/abs/2112.12371
  - title: 'CalFAT: Calibrated Federated Adversarial Training with Label Skewness'
    affiliation: ZJU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=8N1NDRGQSQ
      PDF: https://arxiv.org/abs/2205.14926
  - title: 'SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max
      Learning'
    affiliation: OSU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=wTp4KgVIJ5
      PDF: https://arxiv.org/abs/2210.00611
  - title: Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance)
      Noise in Federated Learning
    affiliation: OSU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=8SilFGuXgmk
      PDF: https://arxiv.org/abs/2210.00690
  - title: Personalized Federated Learning towards Communication Efficiency, Robustness
      and Fairness
    affiliation: PKU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=wFymjzZEEkH
  - title: Federated Submodel Optimization for Hot and Cold Data Features
    affiliation: SJTU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=sj9l1JCrAk6
  - title: 'BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent
      Kernels'
    affiliation: UC Berkeley
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=jzd2bE5MxW
      PDF: https://arxiv.org/abs/2207.06343
  - title: Byzantine-tolerant federated Gaussian process regression for streaming
      data
    affiliation: PSU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Nx4gNemvNvx
      CODE: https://openreview.net/attachment?id=Nx4gNemvNvx&name=SUPP_material
  - title: 'SoteriaFL: A Unified Framework for Private Federated Learning with Communication
      Compression'
    affiliation: CMU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=tz1PRT6lfLe
      PDF: https://arxiv.org/abs/2206.09888
  - title: 'Coresets for Vertical Federated Learning: Regularized Linear Regression
      and K-Means Clustering'
    affiliation: Yale
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=N0tKCpMhA2
      PDF: https://arxiv.org/abs/2210.14664
      CODE: https://github.com/haoyuzhao123/coreset-vfl-codes
  - title: Communication Efficient Federated Learning for Generalized Linear Bandits
    affiliation: University of Virginia
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Xwz9B6LDM5c
      CODE: https://openreview.net/attachment?id=Xwz9B6LDM5c&name=SUPP_material
  - title: Recovering Private Text in Federated Learning of Language Models
    affiliation: Princeton
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=dqgzfhHd2-
      PDF: https://arxiv.org/abs/2205.08514
      CODE: https://github.com/Princeton-SysML/FILM
  - title: 'Federated Learning from Pre-Trained Models: A Contrastive Learning Approach'
    affiliation: UTS
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=mhQLcMjWw75
      PDF: https://arxiv.org/abs/2209.10083
  - title: Global Convergence of Federated Learning for Mixed Regression
    affiliation: Northeastern University
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=DdxNka9tMRd
      PDF: https://arxiv.org/abs/2206.07279
  - title: Resource-Adaptive Federated Learning with All-In-One Neural Composition
    affiliation: JHU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=wfel7CjOYk
  - title: Self-Aware Personalized Federated Learning
    affiliation: Amazon
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=EqJ5_hZSqgy
      PDF: https://arxiv.org/abs/2204.08069
  - title: A Communication-efficient Algorithm with Linear Convergence for Federated
      Minimax Learning
    affiliation: Northeastern University
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=TATzsweWfof
      PDF: https://arxiv.org/abs/2206.01132
  - title: An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal
      Effects
    affiliation: NUS
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=fJt2KFnRqZ
  - title: Sharper Convergence Guarantees for Asynchronous SGD for Distributed and
      Federated Learning
    affiliation: EPFL
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=4_oCZgBIVI
      PDF: https://arxiv.org/abs/2206.08307
  - title: Personalized Online Federated Multi-Kernel Learning
    affiliation: UCI
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=wUctlvhsNWg
  - title: 'SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with
      Alternate Training'
    affiliation: Duke University
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=1GAjC_FauE
      PDF: https://arxiv.org/abs/2106.01432
      CODE: https://openreview.net/attachment?id=1GAjC_FauE&name=SUPP_material
  - title: A Unified Analysis of Federated Learning with Arbitrary Client Participation
    affiliation: IBM
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=qSs7C7c4G8D
      PDF: https://arxiv.org/abs/2205.13648
  - title: Preservation of the Global Knowledge by Not-True Distillation in Federated
      Learning
    affiliation: KAIST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=qw3MZb1Juo
      PDF: https://arxiv.org/abs/2106.03097
      CODE: https://openreview.net/attachment?id=qw3MZb1Juo&name=SUPP_material
  - title: 'FedSR: A Simple and Effective Domain Generalization Method for Federated
      Learning'
    affiliation: University of Oxford
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=mrt90D00aQX
      CODE: https://openreview.net/attachment?id=mrt90D00aQX&name=SUPP_material
  - title: 'Factorized-FL: Personalized Federated Learning with Parameter Factorization
      & Similarity Matching'
    affiliation: KAIST
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Ql75oqz1npy
      PDF: https://arxiv.org/abs/2202.00270
      CODE: https://openreview.net/attachment?id=Ql75oqz1npy&name=SUPP_material
  - title: A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual
      Linear Bandits
    affiliation: UC
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Fx7oXUVEPW
      PDF: https://arxiv.org/abs/2207.03106
  - title: 'Learning to Attack Federated Learning: A Model-based Reinforcement Learning
      Attack Framework'
    affiliation: Tulane University
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=4OHRr7gmhd4
  - title: On Privacy and Personalization in Cross-Silo Federated Learning
    affiliation: CMU
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Oq2bdIQQOIZ
      PDF: https://arxiv.org/abs/2206.07902
  - title: A Coupled Design of Exploiting Record Similarity for Practical Vertical
      Federated Learning
    affiliation: NUS
    venue: NeurIPS
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=fiBnhdazkyx
      PDF: https://arxiv.org/abs/2106.06312
      CODE: https://github.com/Xtra-Computing/FedSim
  - title: 'FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic
      Healthcare Settings'
    affiliation: Owkin
    venue: NeurIPS Datasets and Benchmarks
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=GgM5DiAb6A2
      CODE: https://github.com/owkin/FLamby
  - title: A Tree-based Model Averaging Approach for Personalized Treatment Effect
      Estimation from Heterogeneous Data Sources
    affiliation: University of Pittsburgh
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/tan22a.html
      PDF: https://arxiv.org/abs/2103.06261
      CODE: https://github.com/ellenxtan/ifedtree
  - title: Fast Composite Optimization and Statistical Recovery in Federated Learning
    affiliation: SJTU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/bao22b.html
      PDF: https://arxiv.org/abs/2207.08204
      CODE: https://github.com/MingruiLiu-ML-Lab/Federated-Sparse-Learning
  - title: Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
    affiliation: NYU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/bietti22a.html
      PDF: https://arxiv.org/abs/2202.05318
      CODE: https://github.com/albietz/ppsgd
  - title: 'The Fundamental Price of Secure Aggregation in Differentially Private
      Federated Learning :fire:'
    affiliation: Stanford; Google Research
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/chen22c.html
      PDF: https://arxiv.org/abs/2203.03761
      CODE: https://github.com/google-research/federated/tree/master/private_linear_compression
      SLIDE: https://icml.cc/media/icml-2022/Slides/17529.pdf
  - title: The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure
      Aggregation
    affiliation: Stanford; Google Research
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/chen22s.html
      PDF: https://arxiv.org/abs/2207.09916
      CODE: https://github.com/WeiNingChen/pbm
  - title: 'DisPFL: Towards Communication-Efficient Personalized Federated Learning
      via Decentralized Sparse Training'
    affiliation: USTC
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/dai22b.html
      PDF: https://arxiv.org/abs/2206.00187
      CODE: https://github.com/rong-dai/DisPFL
  - title: 'FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method
      for Federated Learning'
    affiliation: University of Oulu
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/elgabli22a.html
      PDF: https://arxiv.org/abs/2206.08829
      CODE: https://github.com/aelgabli/FedNew
  - title: 'DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated
      Learning'
    affiliation: University of Cambridge
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/honig22a.html
      PDF: https://arxiv.org/abs/2111.00465
      SLIDE: https://icml.cc/media/icml-2022/Slides/16009.pdf
      CODE: https://media.icml.cc/Conferences/ICML2022/SUPP/honig22a-supp.zip
  - title: Accelerated Federated Learning with Decoupled Adaptive Optimization
    affiliation: Auburn University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/jin22e.html
      PDF: https://arxiv.org/abs/2207.07223
  - title: 'Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling'
    affiliation: Georgia Tech
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/khodadadian22a.html
      PDF: https://arxiv.org/abs/2206.10185
  - title: Multi-Level Branched Regularization for Federated Learning
    affiliation: Seoul National University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/kim22a.html
      PDF: https://arxiv.org/abs/2207.06936
      CODE: https://github.com/jinkyu032/FedMLB
      PAGE: http://cvlab.snu.ac.kr/research/FedMLB/
  - title: 'FedScale: Benchmarking Model and System Performance of Federated Learning
      at Scale :fire:'
    affiliation: University of Michigan
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/lai22a.html
      PDF: https://arxiv.org/abs/2105.11367
      CODE: https://github.com/SymbioticLab/FedScale
  - title: Federated Learning with Positive and Unlabeled Data
    affiliation: XJTU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/lin22b.html
      PDF: https://arxiv.org/abs/2106.10904
      CODE: https://github.com/littlesunlxy/fedpu-torch
  - title: Deep Neural Network Fusion via Graph Matching with Applications to Model
      Ensemble and Federated Learning
    affiliation: SJTU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/liu22k.html
      CODE: https://github.com/Thinklab-SJTU/GAMF
  - title: 'Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering'
    affiliation: University of Michigan
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/lubana22a.html
      PDF: https://arxiv.org/abs/2205.11506
      CODE: https://github.com/akhilmathurs/orchestra
  - title: Disentangled Federated Learning for Tackling Attributes Skew via Invariant
      Aggregation and Diversity Transferring
    affiliation: USTC
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/luo22b.html
      PDF: https://arxiv.org/abs/2206.06818
      CODE: https://github.com/luozhengquan/DFL
      SLIDE: https://icml.cc/media/icml-2022/Slides/16881.pdf
      解读: https://www.bilibili.com/read/cv17092678
  - title: Architecture Agnostic Federated Learning for Neural Networks
    affiliation: The University of Texas at Austin
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/makhija22a.html
      PDF: https://proceedings.mlr.press/v162/zhang22p.html
      SLIDE: https://icml.cc/media/icml-2022/Slides/16926.pdf
  - title: Personalized Federated Learning through Local Memorization
    affiliation: Inria
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/marfoq22a.html
      PDF: https://arxiv.org/abs/2111.09360
      CODE: https://github.com/omarfoq/knn-per
  - title: Proximal and Federated Random Reshuffling
    affiliation: KAUST
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/mishchenko22a.html
      PDF: https://arxiv.org/abs/2102.06704
      CODE: https://github.com/konstmish/rr_prox_fed
  - title: Federated Learning with Partial Model Personalization
    affiliation: University of Washington
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/pillutla22a.html
      PDF: https://arxiv.org/abs/2204.03809
      CODE: https://github.com/krishnap25/FL_partial_personalization
  - title: Generalized Federated Learning via Sharpness Aware Minimization
    affiliation: University of South Florida
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/qu22a.html
      PDF: https://arxiv.org/abs/2206.02618
  - title: 'FedNL: Making Newton-Type Methods Applicable to Federated Learning'
    affiliation: KAUST
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/safaryan22a.html
      PDF: https://arxiv.org/abs/2106.02969
      VIDEO: https://www.youtube.com/watch?v=_VYCEWT17R0&ab_channel=FederatedLearningOneWorldSeminar
      SLIDE: https://icml.cc/media/icml-2022/Slides/17084.pdf
  - title: 'Federated Minimax Optimization: Improved Convergence Analyses and Algorithms'
    affiliation: CMU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/sharma22c.html
      PDF: https://arxiv.org/abs/2203.04850
      SLIDE: https://icml.cc/media/icml-2022/Slides/17435.pdf
  - title: 'Virtual Homogeneity Learning: Defending against Data Heterogeneity in
      Federated Learning'
    affiliation: Hong Kong Baptist University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/tang22d.html
      PDF: https://arxiv.org/abs/2206.02465
      CODE: https://github.com/wizard1203/VHL
      解读: https://zhuanlan.zhihu.com/p/548508633
  - title: 'FedNest: Federated Bilevel, Minimax, and Compositional Optimization'
    affiliation: University of Michigan
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/tarzanagh22a.html
      PDF: https://arxiv.org/abs/2205.02215
      CODE: https://github.com/mc-nya/FedNest
  - title: 'EDEN: Communication-Efficient and Robust Distributed Mean Estimation for
      Federated Learning'
    affiliation: VMware Research
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/vargaftik22a.html
      PDF: https://arxiv.org/abs/2108.08842
      CODE: https://github.com/amitport/EDEN-Distributed-Mean-Estimation
  - title: Communication-Efficient Adaptive Federated Learning
    affiliation: Pennsylvania State University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/wang22o.html
      PDF: https://arxiv.org/abs/2205.02719
  - title: 'ProgFed: Effective, Communication, and Computation Efficient Federated
      Learning by Progressive Training'
    affiliation: CISPA Helmholz Center for Information Security
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/wang22y.html
      PDF: https://arxiv.org/abs/2110.05323
      SLIDE: https://icml.cc/media/icml-2022/Slides/16194_hmjFNsN.pdf
      CODE: https://github.com/a514514772/ProgFed
  - title: 'Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
      :fire:'
    affiliation: University of Maryland
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/wen22a.html
      PDF: https://arxiv.org/abs/2202.00580
      CODE: https://github.com/JonasGeiping/breaching
  - title: Anarchic Federated Learning
    affiliation: The Ohio State University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/yang22r.html
      PDF: https://arxiv.org/abs/2108.09875
  - title: 'QSFL: A Two-Level Uplink Communication Optimization Framework for Federated
      Learning'
    affiliation: Nankai University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/yi22a.html
      CODE: https://github.com/LipingYi/QSFL
  - title: Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
    affiliation: KAIST
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/yoon22a.html
      PDF: https://arxiv.org/abs/2202.11453
  - title: Neural Tangent Kernel Empowered Federated Learning
    affiliation: NC State University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/yue22a.html
      PDF: https://arxiv.org/abs/2110.03681
      CODE: https://github.com/KAI-YUE/ntk-fed
  - title: 'Understanding Clipping for Federated Learning: Convergence and Client-Level
      Differential Privacy'
    affiliation: UMN
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/zhang22b.html
      PDF: https://arxiv.org/abs/2106.13673
  - title: Personalized Federated Learning via Variational Bayesian Inference
    affiliation: CAS
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/zhang22o.html
      PDF: https://arxiv.org/abs/2206.07977
      SLIDE: https://icml.cc/media/icml-2022/Slides/17302.pdf
      UC.: https://github.com/AllenBeau/pFedBayes
  - title: Federated Learning with Label Distribution Skew via Logits Calibration
    affiliation: ZJU
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/zhang22p.html
  - title: 'Neurotoxin: Durable Backdoors in Federated Learning'
    affiliation: Southeast University;Princeton
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/zhang22w.html
      PDF: https://arxiv.org/abs/2206.10341
      CODE: https://github.com/jhcknzzm/Federated-Learning-Backdoor/
  - title: Resilient and Communication Efficient Learning for Heterogeneous Federated
      Systems
    affiliation: Michigan State University
    venue: ICML
    year: '2022'
    materials:
      PUB: https://proceedings.mlr.press/v162/zhu22e.html
  - title: 'Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond'
    affiliation: KAIST
    venue: ICLR (oral)
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=LdlwbBP2mlq
      CODE: https://openreview.net/attachment?id=LdlwbBP2mlq&name=SUPP_material
  - title: Bayesian Framework for Gradient Leakage
    affiliation: ETH Zurich
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=f2lrIbGx3x7
      PDF: https://arxiv.org/abs/2111.04706
      CODE: https://github.com/eth-sri/bayes-framework-leakage
  - title: Federated Learning from only unlabeled data with class-conditional-sharing
      clients
    affiliation: The University of Tokyo; CUHK
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=WHA8009laxu
      CODE: https://github.com/lunanbit/FedUL
  - title: 'FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated
      Learning'
    affiliation: CMU; University of Illinois at Urbana-Champaign; University of Washington
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=ZaVVVlcdaN
      PDF: https://arxiv.org/abs/2108.06869.
  - title: Acceleration of Federated Learning with Alleviated Forgetting in Local
      Training
    affiliation: THU
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=541PxiEKN3F
      PDF: https://arxiv.org/abs/2203.02645
      CODE: https://github.com/Zoesgithub/FedReg
  - title: 'FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated
      Learning'
    affiliation: POSTECH
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=d71n4ftoCBy
      PDF: https://arxiv.org/abs/2108.06098
      CODE: https://github.com/South-hw/FedPara_ICLR22
  - title: An Agnostic Approach to Federated Learning with Class Imbalance
    affiliation: University of Pennsylvania
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=Xo0lbDt975
      CODE: https://github.com/shenzebang/Federated-Learning-Pytorch
  - title: Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
    affiliation: Michigan State University; The University of Texas at Austin
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=_QLmakITKg
      PDF: https://arxiv.org/abs/2203.09747
      CODE: https://github.com/illidanlab/SplitMix
  - title: 'Robbing the Fed: Directly Obtaining Private Data in Federated Learning
      with Modified Models :fire:'
    affiliation: University of Maryland; NYU
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=fwzUgo0FM9v
      PDF: https://arxiv.org/abs/2110.13057
      CODE: https://github.com/JonasGeiping/breaching
  - title: 'ZeroFL: Efficient On-Device Training for Federated Learning with Local
      Sparsity'
    affiliation: University of Cambridge; University of Oxford
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=2sDQwC_hmnM
      PDF: https://arxiv.org/abs/2208.02507
  - title: Diverse Client Selection for Federated Learning via Submodular Maximization
    affiliation: Intel; CMU
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=nwKXyFvaUm
      CODE: https://github.com/melodi-lab/divfl
  - title: 'Recycling Model Updates in Federated Learning: Are Gradient Subspaces
      Low-Rank?'
    affiliation: Purdue
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=B7ZbqNLDn-_
      PDF: https://arxiv.org/abs/2202.00280
      CODE: https://github.com/shams-sam/FedOptim
  - title: 'Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from
      Periodically Shifting Distributions :fire:'
    affiliation: University of Maryland; Google
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=E4EE_ohFGz
      CODE: https://github.com/google-research/federated/tree/7525c36324cb022bc05c3fce88ef01147cae9740/periodic_distribution_shift
  - title: Towards Model Agnostic Federated Learning Using Knowledge Distillation
    affiliation: EPFL
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=lQI_mZjvBxj
      PDF: https://arxiv.org/abs/2110.15210
      CODE: https://github.com/AfoninAndrei/ICLR2022
  - title: Divergence-aware Federated Self-Supervised Learning
    affiliation: NTU; SenseTime
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=oVE1z8NlNe
      PDF: https://arxiv.org/abs/2204.04385
      CODE: https://github.com/EasyFL-AI/EasyFL
  - title: 'What Do We Mean by Generalization in Federated Learning? :fire:'
    affiliation: Stanford; Google
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=VimqQq-i_Q
      PDF: https://arxiv.org/abs/2110.14216
      CODE: https://github.com/google-research/federated/tree/master/generalization
  - title: 'FedBABU: Toward Enhanced Representation for Federated Image Classification'
    affiliation: KAIST
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=HuaYQfggn5u
      PDF: https://arxiv.org/abs/2106.06042
      CODE: https://github.com/jhoon-oh/FedBABU
  - title: Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
    affiliation: EPFL
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=jXKKDEi5vJt
      PDF: https://arxiv.org/abs/2006.09365
      CODE: https://github.com/liehe/byzantine-robust-noniid-optimizer
  - title: Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters
    affiliation: Aibee
    venue: ICLR Spotlight
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=7l1IjZVddDW
      PDF: https://arxiv.org/abs/2201.12467
      PAGE: https://irvingmeng.github.io/projects/privacyface/
      解读: https://zhuanlan.zhihu.com/p/484920301
  - title: Hybrid Local SGD for Federated Learning with Heterogeneous Communications
    affiliation: University of Texas; Pennsylvania State University
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=H0oaWl6THa
  - title: On Bridging Generic and Personalized Federated Learning for Image Classification
    affiliation: The Ohio State University
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=I1hQbx10Kxn
      PDF: https://arxiv.org/abs/2107.00778
      CODE: https://github.com/hongyouc/Fed-RoD
  - title: 'Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond'
    affiliation: KAIST; MIT
    venue: ICLR
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=LdlwbBP2mlq
      PDF: https://arxiv.org/abs/2110.10342
  - title: 'One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve
      Them.'
    affiliation: ''
    venue: JMLR
    year: '2021'
    materials:
      PUB: http://jmlr.org/papers/v22/19-1048.html
      CODE: https://github.com/sabersalehk/MRE_C
  - title: Constrained differentially private federated learning for low-bandwidth
      devices
    affiliation: ''
    venue: UAI
    year: '2021'
    materials:
      PUB: https://proceedings.mlr.press/v161/kerkouche21a.html
      PDF: https://arxiv.org/abs/2103.00342
  - title: Federated stochastic gradient Langevin dynamics
    affiliation: ''
    venue: UAI
    year: '2021'
    materials:
      PUB: https://proceedings.mlr.press/v161/mekkaoui21a.html
      PDF: https://arxiv.org/abs/2004.11231
  - title: Federated Learning Based on Dynamic Regularization
    affiliation: BU; ARM
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=B7v4QMR6Z9w
      PDF: https://arxiv.org/abs/2111.04263
      CODE: https://github.com/AntixK/FedDyn
  - title: Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated
      Learning
    affiliation: The Ohio State University
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=jDdzh5ul-d
      PDF: https://arxiv.org/abs/2101.11203
  - title: 'HeteroFL: Computation and Communication Efficient Federated Learning for
      Heterogeneous Clients'
    affiliation: Duke University
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=TNkPBBYFkXg
      PDF: https://arxiv.org/abs/2010.01264
      CODE: https://github.com/dem123456789/HeteroFL-Computation-and-Communication-Efficient-Federated-Learning-for-Heterogeneous-Clients
  - title: 'FedMix: Approximation of Mixup under Mean Augmented Federated Learning'
    affiliation: KAIST
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=Ogga20D2HO-
      PDF: https://arxiv.org/abs/2107.00233
  - title: 'Federated Learning via Posterior Averaging: A New Perspective and Practical
      Algorithms :fire:'
    affiliation: CMU; Google
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=GFsU8a0sGB
      PDF: https://arxiv.org/abs/2010.05273
      CODE: https://github.com/alshedivat/fedpa
  - title: 'Adaptive Federated Optimization :fire:'
    affiliation: Google
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=LkFG3lB13U5
      PDF: https://arxiv.org/abs/2003.00295
      CODE: https://github.com/google-research/federated/tree/master/optimization
  - title: Personalized Federated Learning with First Order Model Optimization
    affiliation: Stanford; NVIDIA
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=ehJqJQk9cw
      PDF: https://arxiv.org/abs/2012.08565
      CODE: https://github.com/NVlabs/FedFomo
      UC.: https://github.com/TsingZ0/PFL-Non-IID
  - title: 'FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
      :fire:'
    affiliation: Princeton
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=6YEQUn0QICG
      PDF: https://arxiv.org/abs/2102.07623
      CODE: https://github.com/med-air/FedBN
  - title: 'FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning'
    affiliation: The Ohio State University
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=dgtpE6gKjHn
      PDF: https://arxiv.org/abs/2009.01974
      CODE: https://github.com/hongyouc/fedbe
  - title: Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint
      Learning
    affiliation: KAIST
    venue: ICLR
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=ce6CFXBh30h
      PDF: https://arxiv.org/abs/2006.12097
      CODE: https://github.com/wyjeong/FedMatch
  - title: 'KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge
      Distillation'
    affiliation: ZJU
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/feng21f.html
      PDF: https://arxiv.org/abs/2011.09757
      CODE: https://github.com/FengHZ/KD3A
      解读: https://mp.weixin.qq.com/s/gItgiZmKUxg0ltaeOVdnRw
  - title: 'Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing
      the User Participant Matrix'
    affiliation: Harvard University
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/lam21b.html
      PDF: https://arxiv.org/abs/2106.06089
      VIDEO: https://slideslive.com/38958558/gradient-disaggregation-breaking-privacy-in-federated-learning-by-reconstructing-the-user-participant-matrix
      CODE: https://github.com/gdisag/gradient_disaggregation
  - title: 'FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning
      Analysis'
    affiliation: PKU; Princeton
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/huang21c.html
      PDF: https://arxiv.org/abs/2105.05001
      VIDEO: https://slideslive.com/38959650/flntk-a-neural-tangent-kernelbased-framework-for-federated-learning-analysis
  - title: 'Personalized Federated Learning using Hypernetworks :fire:'
    affiliation: Bar-Ilan University; NVIDIA
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/shamsian21a.html
      PDF: https://arxiv.org/abs/2103.04628
      CODE: https://github.com/AvivSham/pFedHN
      PAGE: https://avivsham.github.io/pfedhn/
      VIDEO: https://slideslive.com/38959583/personalized-federated-learning-using-hypernetworks
      解读: https://zhuanlan.zhihu.com/p/431130945
  - title: Federated Composite Optimization
    affiliation: Stanford; Google
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/yuan21d.html
      PDF: https://arxiv.org/abs/2011.08474
      CODE: https://github.com/hongliny/FCO-ICML21
      VIDEO: https://www.youtube.com/watch?v=tKDbc60XJks&ab_channel=FederatedLearningOneWorldSeminar
      SLIDE: https://hongliny.github.io/files/FCO_ICML21/FCO_ICML21_slides.pdf
  - title: Exploiting Shared Representations for Personalized Federated Learning
    affiliation: University of Texas at Austin; University of Pennsylvania
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/collins21a.html
      PDF: https://arxiv.org/abs/2102.07078
      CODE: https://github.com/lgcollins/FedRep
      VIDEO: https://slideslive.com/38959519/exploiting-shared-representations-for-personalized-federated-learning
  - title: 'Data-Free Knowledge Distillation for Heterogeneous Federated Learning
      :fire:'
    affiliation: Michigan State University
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/zhu21b.html
      PDF: https://arxiv.org/abs/2105.10056
      CODE: https://github.com/zhuangdizhu/FedGen
      VIDEO: https://slideslive.com/38959429/datafree-knowledge-distillation-for-heterogeneous-federated-learning
  - title: Federated Continual Learning with Weighted Inter-client Transfer
    affiliation: KAIST
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/yoon21b.html
      PDF: https://arxiv.org/abs/2003.03196
      CODE: https://github.com/wyjeong/FedWeIT
      VIDEO: https://slideslive.com/38959323/federated-continual-learning-with-weighted-interclient-transfer
  - title: Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication
      Complexity
    affiliation: The University of Iowa
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/yuan21a.html
      PDF: https://arxiv.org/abs/2102.04635
      CODE: https://libauc.org/
      VIDEO: https://slideslive.com/38959235/federated-deep-auc-maximization-for-hetergeneous-data-with-a-constant-communication-complexity
  - title: Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
    affiliation: The University of Tokyo
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/murata21a.html
      PDF: https://arxiv.org/abs/2102.03198
      VIDEO: https://slideslive.com/38959169/biasvariance-reduced-local-sgd-for-less-heterogeneous-federated-learning
  - title: Federated Learning of User Verification Models Without Sharing Embeddings
    affiliation: Qualcomm
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/hosseini21a.html
      PDF: https://arxiv.org/abs/2104.08776
      VIDEO: https://slideslive.com/38958858/federated-learning-of-user-verification-models-without-sharing-embeddings
  - title: 'Clustered Sampling: Low-Variance and Improved Representativity for Clients
      Selection in Federated Learning'
    affiliation: Accenture
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/fraboni21a.html
      PDF: https://arxiv.org/abs/2105.05883
      CODE: https://github.com/Accenture//Labs-Federated-Learning/tree/clustered_sampling
      VIDEO: https://slideslive.com/38959618/clustered-sampling-lowvariance-and-improved-representativity-for-clients-selection-in-federated-learning
  - title: 'Ditto: Fair and Robust Federated Learning Through Personalization'
    affiliation: CMU; Facebook AI
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/li21h.html
      PDF: https://arxiv.org/abs/2012.04221
      CODE: https://github.com/litian96/ditto
      VIDEO: https://slideslive.com/38955195/ditto-fair-and-robust-federated-learning-through-personalization
  - title: 'Heterogeneity for the Win: One-Shot Federated Clustering'
    affiliation: CMU
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/dennis21a.html
      PDF: https://arxiv.org/abs/2103.00697
      VIDEO: https://slideslive.com/38959380/heterogeneity-for-the-win-oneshot-federated-clustering
  - title: 'The Distributed Discrete Gaussian Mechanism for Federated Learning with
      Secure Aggregation :fire:'
    affiliation: Google
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/kairouz21a.html
      PDF: https://arxiv.org/abs/2102.06387
      CODE: https://github.com/google-research/federated/tree/master/distributed_dp
      VIDEO: https://slideslive.com/38959306/the-distributed-discrete-gaussian-mechanism-for-federated-learning-with-secure-aggregation
  - title: Debiasing Model Updates for Improving Personalized Federated Training
    affiliation: BU; Arm
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/acar21a.html
      CODE: https://github.com/venkatesh-saligrama/Personalized-Federated-Learning
      VIDEO: https://slideslive.com/38959212/debiasing-model-updates-for-improving-personalized-federated-training
  - title: 'One for One, or All for All: Equilibria and Optimality of Collaboration
      in Federated Learning'
    affiliation: Toyota; Berkeley; Cornell University
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/blum21a.html
      PDF: https://arxiv.org/abs/2103.03228
      CODE: https://github.com/rlphilli/Collaborative-Incentives
      VIDEO: https://slideslive.com/38959135/one-for-one-or-all-for-all-equilibria-and-optimality-of-collaboration-in-federated-learning
  - title: 'CRFL: Certifiably Robust Federated Learning against Backdoor Attacks'
    affiliation: UIUC; IBM
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/xie21a.html
      PDF: https://arxiv.org/abs/2106.08283
      CODE: https://github.com/AI-secure/CRFL
      VIDEO: https://slideslive.com/38959047/crfl-certifiably-robust-federated-learning-against-backdoor-attacks
  - title: Federated Learning under Arbitrary Communication Patterns
    affiliation: Indiana University; Amazon
    venue: ICML
    year: '2021'
    materials:
      PUB: http://proceedings.mlr.press/v139/avdiukhin21a.html
      VIDEO: https://slideslive.com/38959048/federated-learning-under-arbitrary-communication-patterns
  - title: 'CANITA: Faster Rates for Distributed Convex Optimization with Communication
      Compression'
    affiliation: CMU
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=eNB4WXnNczJ
      PDF: https://arxiv.org/abs/2107.09461
  - title: Boosting with Multiple Sources
    affiliation: Google
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=1oP1duoZxx
  - title: 'DRIVE: One-bit Distributed Mean Estimation'
    affiliation: VMware
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=KXRTmcv3dQ8
      CODE: https://github.com/amitport/DRIVE-One-bit-Distributed-Mean-Estimation
  - title: Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine
      Learning
    affiliation: NUS
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=yRfsADObu18
      CODE: https://github.com/XinyiYS/Gradient-Driven-Rewards-to-Guarantee-Fairness-in-Collaborative-Machine-Learning
  - title: Gradient Inversion with Generative Image Prior
    affiliation: POSTECH
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/fa84632d742f2729dc32ce8cb5d49733-Abstract.html
      PDF: https://arxiv.org/abs/2110.14962
      CODE: https://github.com/ml-postech/gradient-inversion-generative-image-prior
  - title: Distributed Machine Learning with Sparse Heterogeneous Data
    affiliation: University of Oxford
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=F9HNBbytcqT
      PDF: https://arxiv.org/abs/1912.01417
  - title: Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed
      Learning
    affiliation: UCLA
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://openreview.net/forum?id=SPrVNsXnGd
      PDF: https://arxiv.org/abs/2107.08763
  - title: 'Sageflow: Robust Federated Learning against Both Stragglers and Adversaries'
    affiliation: KAIST
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2021/hash/076a8133735eb5d7552dc195b125a454-Abstract.html
  - title: 'CAFE: Catastrophic Data Leakage in Vertical Federated Learning'
    affiliation: Rensselaer Polytechnic Institute; IBM Research
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/08040837089cdf46631a10aca5258e16-Abstract.html
      CODE: https://github.com/DeRafael/CAFE
  - title: Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
    affiliation: NUS
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/080acdcce72c06873a773c4311c2e464-Abstract.html
      PDF: https://arxiv.org/abs/2110.14074
      CODE: https://github.com/flint-xf-fan/Byzantine-Federeated-RL
  - title: 'Optimality and Stability in Federated Learning: A Game-theoretic Approach'
    affiliation: Cornell University
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/09a5e2a11bea20817477e0b1dfe2cc21-Abstract.html
      PDF: https://arxiv.org/abs/2106.09580
      CODE: https://github.com/kpdonahue/model_sharing_games
  - title: 'QuPeD: Quantized Personalization via Distillation with Applications to
      Federated Learning'
    affiliation: UCLA
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/1dba3025b159cd9354da65e2d0436a31-Abstract.html
      PDF: https://arxiv.org/abs/2107.13892
      CODE: https://github.com/zkhku/fedsage
      解读: https://zhuanlan.zhihu.com/p/430789355
  - title: 'The Skellam Mechanism for Differentially Private Federated Learning :fire:'
    affiliation: Google Research; CMU
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/285baacbdf8fda1de94b19282acd23e2-Abstract.html
      PDF: https://arxiv.org/abs/2110.04995
      CODE: https://github.com/google-research/federated/tree/master/distributed_dp
  - title: 'No Fear of Heterogeneity: Classifier Calibration for Federated Learning
      with Non-IID Data'
    affiliation: NUS; Huawei
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/2f2b265625d76a6704b08093c652fd79-Abstract.html
      PDF: https://arxiv.org/abs/2106.05001
  - title: 'STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal
      Sample and Communication Complexities for Federated Learning'
    affiliation: UMN
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/3016a447172f3045b65f5fc83e04b554-Abstract.html
      PDF: https://arxiv.org/abs/2106.10435
  - title: Subgraph Federated Learning with Missing Neighbor Generation
    affiliation: Emory;  UBC; Lehigh University
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/34adeb8e3242824038aa65460a47c29e-Abstract.html
      PDF: https://arxiv.org/abs/2106.13430
      CODE: https://github.com/zkhku/fedsage
      解读: https://zhuanlan.zhihu.com/p/423555171
  - title: 'Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
      :fire:'
    affiliation: Princeton
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.neurips.cc/paper/2021/hash/3b3fff6463464959dcd1b68d0320f781-Abstract.html
      PDF: https://arxiv.org/abs/2112.00059
      CODE: https://github.com/Princeton-SysML/GradAttack
  - title: Personalized Federated Learning With Gaussian Processes
    affiliation: Bar-Ilan University
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2021/hash/46d0671dd4117ea366031f87f3aa0093-Abstract.html
      PDF: https://arxiv.org/abs/2106.15482
      CODE: https://github.com/IdanAchituve/pFedGP
  - title: Differentially Private Federated Bayesian Optimization with Distributed
      Exploration
    affiliation: MIT; NUS
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/4c27cea8526af8cfee3be5e183ac9605-Abstract.html
      PDF: https://arxiv.org/abs/2110.14153
      CODE: https://github.com/daizhongxiang/Differentially-Private-Federated-Bayesian-Optimization
  - title: Parameterized Knowledge Transfer for Personalized Federated Learning
    affiliation: PolyU
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/5383c7318a3158b9bc261d0b6996f7c2-Abstract.html
      PDF: https://arxiv.org/abs/2111.02862
      CODE: https://github.com/cugzj/KT-pFL
  - title: 'Federated Reconstruction: Partially Local Federated Learning :fire:'
    affiliation: Google Research
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/5d44a2b0d85aa1a4dd3f218be6422c66-Abstract.html
      PDF: https://arxiv.org/abs/2102.03448
      CODE: https://github.com/google-research/federated/tree/master/reconstruction
      UC.: https://github.com/KarhouTam/FedRecon
  - title: Fast Federated Learning in the Presence of Arbitrary Device Unavailability
    affiliation: THU; Princeton; MIT
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/64be20f6dd1dd46adf110cf871e3ed35-Abstract.html
      PDF: https://arxiv.org/abs/2106.04159
      CODE: https://github.com/hmgxr128/MIFA_code/
  - title: 'FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated
      Learning from a Client Perspective'
    affiliation: Duke University; Accenture Labs
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/692baebec3bb4b53d7ebc3b9fabac31b-Abstract.html
      PDF: https://arxiv.org/abs/2110.13864
      CODE: https://github.com/jeremy313/FL-WBC
  - title: 'FjORD: Fair and Accurate Federated Learning under heterogeneous targets
      with Ordered Dropout'
    affiliation: KAUST; Samsung AI Center
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/6aed000af86a084f9cb0264161e29dd3-Abstract.html
      PDF: https://arxiv.org/abs/2102.13451
  - title: 'Linear Convergence in Federated Learning: Tackling Client Heterogeneity
      and Sparse Gradients'
    affiliation: University of Pennsylvania
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/7a6bda9ad6ffdac035c752743b7e9d0e-Abstract.html
      PDF: https://arxiv.org/abs/2102.07053
      VIDEO: https://papertalk.org/papertalks/35898
  - title: Federated Multi-Task Learning under a Mixture of Distributions
    affiliation: INRIA; Accenture Labs
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/82599a4ec94aca066873c99b4c741ed8-Abstract.html
      PDF: https://arxiv.org/abs/2108.10252
      CODE: https://github.com/omarfoq/FedEM
  - title: Federated Graph Classification over Non-IID Graphs
    affiliation: Emory
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/9c6947bd95ae487c81d4e19d3ed8cd6f-Abstract.html
      PDF: https://arxiv.org/abs/2106.13423
      CODE: https://github.com/Oxfordblue7/GCFL
      解读: https://zhuanlan.zhihu.com/p/430718887
  - title: 'Federated Hyperparameter Tuning: Challenges, Baselines, and Connections
      to Weight-Sharing'
    affiliation: CMU; Hewlett Packard Enterprise
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/a0205b87490c847182672e8d371e9948-Abstract.html
      PDF: https://arxiv.org/abs/2106.04502
      CODE: https://github.com/mkhodak/fedex
  - title: 'On Large-Cohort Training for Federated Learning :fire:'
    affiliation: Google; CMU
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/ab9ebd57177b5106ad7879f0896685d4-Abstract.html
      PDF: https://arxiv.org/abs/2106.07820
      CODE: https://github.com/google-research/federated/tree/f4e26c1b9b47ac320e520a8b9943ea2c5324b8c2/large_cohort
  - title: 'DeepReduce: A Sparse-tensor Communication Framework for Federated Deep
      Learning'
    affiliation: KAUST; Columbia University; University of Central Florida
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/b0ab42fcb7133122b38521d13da7120b-Abstract.html
      PDF: https://arxiv.org/abs/2102.03112
      CODE: https://github.com/hangxu0304/DeepReduce
  - title: 'PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization'
    affiliation: Huawei
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/c429429bf1f2af051f2021dc92a8ebea-Abstract.html
      VIDEO: https://papertalk.org/papertalks/37327
  - title: Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis
    affiliation: KAIST
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/ceb0595112db2513b9325a85761b7310-Abstract.html
      PDF: https://arxiv.org/abs/2111.01338
  - title: Addressing Algorithmic Disparity and Performance Inconsistency in Federated
      Learning
    affiliation: THU; Alibaba; Weill Cornell Medicine
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/db8e1af0cb3aca1ae2d0018624204529-Abstract.html
      PDF: https://arxiv.org/abs/2108.08435
      CODE: https://github.com/cuis15/FCFL
  - title: Federated Linear Contextual Bandits
    affiliation: The Pennsylvania State University; Facebook; University of Virginia
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/e347c51419ffb23ca3fd5050202f9c3d-Abstract.html
      PDF: https://arxiv.org/abs/2110.14177
      CODE: https://github.com/Ruiquan5514/Federated-Linear-Contextual-Bandits
  - title: Few-Round Learning for Federated Learning
    affiliation: KAIST
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/f065d878ccfb4cc4f4265a4ff8bafa9a-Abstract.html
  - title: Breaking the centralized barrier for cross-device federated learning
    affiliation: EPFL; Google Research
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/f0e6be4ce76ccfa73c5a540d992d0756-Abstract.html
      CODE: https://fedjax.readthedocs.io/en/latest/fedjax.algorithms.html#module-fedjax.algorithms.mime
      VIDEO: https://papertalk.org/papertalks/37564
  - title: Federated-EM with heterogeneity mitigation and variance reduction
    affiliation: Ecole Polytechnique; Google Research
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/f740c8d9c193f16d8a07d3a8a751d13f-Abstract.html
      PDF: https://arxiv.org/abs/2111.02083
  - title: 'Delayed Gradient Averaging: Tolerate the Communication Latency for Federated
      Learning'
    affiliation: MIT; Amazon; Google
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2021/hash/fc03d48253286a798f5116ec00e99b2b-Abstract.html
      PAGE: https://dga.hanlab.ai/
      SLIDE: https://dga.hanlab.ai/assets/dga_slides.pdf
  - title: FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex
      Federated Composite Optimization
    affiliation: University of North Carolina at Chapel Hill; IBM Research
    venue: NeurIPS
    year: '2021'
    materials:
      PUB: https://papers.nips.cc/paper/2021/hash/fe7ee8fc1959cc7214fa21c4840dff0a-Abstract.html
      PDF: https://arxiv.org/abs/2103.03452
      CODE: https://github.com/unc-optimization/FedDR
  - title: Federated Adversarial Domain Adaptation
    affiliation: BU; Columbia University; Rutgers University
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=HJezF3VYPB
      PDF: https://arxiv.org/abs/1911.02054
      CODE: https://drive.google.com/file/d/1OekTpqB6qLfjlE2XUjQPm3F110KDMFc0/view?usp=sharing
  - title: 'DBA: Distributed Backdoor Attacks against Federated Learning'
    affiliation: ZJU; IBM Research
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=rkgyS0VFvr
      CODE: https://github.com/AI-secure/DBA
  - title: 'Fair Resource Allocation in Federated Learning :fire:'
    affiliation: CMU; Facebook AI
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=ByexElSYDr
      PDF: https://arxiv.org/abs/1905.10497
      CODE: https://github.com/litian96/fair_flearn
  - title: 'Federated Learning with Matched Averaging :fire:'
    affiliation: University of Wisconsin-Madison; IBM Research
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=BkluqlSFDS
      PDF: https://arxiv.org/abs/2002.06440
      CODE: https://github.com/IBM/FedMA
  - title: Differentially Private Meta-Learning
    affiliation: CMU
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=rJgqMRVYvr
      PDF: https://proceedings.mlr.press/v162/zhang22p.html
  - title: 'Generative Models for Effective ML on Private, Decentralized Datasets
      :fire:'
    affiliation: Google
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=SJgaRA4FPH
      PDF: https://arxiv.org/abs/1911.06679
      CODE: https://github.com/google-research/federated/tree/master/gans
  - title: 'On the Convergence of FedAvg on Non-IID Data :fire:'
    affiliation: PKU
    venue: ICLR
    year: '2020'
    materials:
      PUB: https://openreview.net/forum?id=HJxNAnVtDS
      PDF: https://arxiv.org/abs/1907.02189
      CODE: https://github.com/lx10077/fedavgpy
      解读: https://zhuanlan.zhihu.com/p/500005337
  - title: 'FedBoost: A Communication-Efficient Algorithm for Federated Learning'
    affiliation: Google
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/hamer20a.html
      VIDEO: https://slideslive.com/38928463/fedboost-a-communicationefficient-algorithm-for-federated-learning?ref=speaker-16993-latest
  - title: 'FetchSGD: Communication-Efficient Federated Learning with Sketching'
    affiliation: UC Berkeley; Johns Hopkins University; Amazon
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/rothchild20a.html
      PDF: https://arxiv.org/abs/2007.07682
      VIDEO: https://slideslive.com/38928454/fetchsgd-communicationefficient-federated-learning-with-sketching
      CODE: https://github.com/kiddyboots216/CommEfficient
  - title: 'SCAFFOLD: Stochastic Controlled Averaging for Federated Learning'
    affiliation: EPFL; Google
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/karimireddy20a.html
      PDF: https://arxiv.org/abs/1910.06378
      VIDEO: https://slideslive.com/38927610/scaffold-stochastic-controlled-averaging-for-federated-learning
      UC.: https://github.com/ramshi236/Accelerated-Federated-Learning-Over-MAC-in-Heterogeneous-Networks
      解读: https://zhuanlan.zhihu.com/p/538941775
  - title: Federated Learning with Only Positive Labels
    affiliation: Google
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/yu20f.html
      PDF: https://arxiv.org/abs/2004.10342
      VIDEO: https://slideslive.com/38928322/federated-learning-with-only-positive-labels
  - title: From Local SGD to Local Fixed-Point Methods for Federated Learning
    affiliation: Moscow Institute of Physics and Technology; KAUST
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/malinovskiy20a.html
      PDF: https://arxiv.org/abs/2004.01442
      SLIDE: https://icml.cc/media/Slides/icml/2020/virtual
      VIDEO: https://slideslive.com/38928320/from-local-sgd-to-local-fixed-point-methods-for-federated-learning
  - title: Acceleration for Compressed Gradient Descent in Distributed and Federated
      Optimization
    affiliation: KAUST
    venue: ICML
    year: '2020'
    materials:
      PUB: http://proceedings.mlr.press/v119/li20g.html
      PDF: https://arxiv.org/abs/2002.11364
      SLIDE: https://icml.cc/media/Slides/icml/2020/virtual
      VIDEO: https://slideslive.com/38927921/acceleration-for-compressed-gradient-descent-in-distributed-optimization
  - title: Differentially-Private Federated Linear Bandits
    affiliation: MIT
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://papers.nips.cc/paper/2020/hash/4311359ed4969e8401880e3c1836fbe1-Abstract.html
      PDF: https://arxiv.org/abs/2010.11425
      CODE: https://github.com/abhimanyudubey/private_federated_linear_bandits
  - title: Federated Principal Component Analysis
    affiliation: University of Cambridge; Quine Technologies
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/47a658229eb2368a99f1d032c8848542-Abstract.html
      PDF: https://arxiv.org/abs/1907.08059
      CODE: https://github.com/andylamp/federated_pca
  - title: 'FedSplit: an algorithmic framework for fast federated optimization'
    affiliation: UC Berkeley
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/4ebd440d99504722d80de606ea8507da-Abstract.html
      PDF: https://arxiv.org/abs/2005.05238
  - title: Federated Bayesian Optimization via Thompson Sampling
    affiliation: NUS; MIT
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/6dfe08eda761bd321f8a9b239f6f4ec3-Abstract.html
      PDF: https://arxiv.org/abs/2010.10154
      CODE: https://github.com/daizhongxiang/Federated_Bayesian_Optimization
  - title: Lower Bounds and Optimal Algorithms for Personalized Federated Learning
    affiliation: KAUST
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/187acf7982f3c169b3075132380986e4-Abstract.html
      PDF: https://arxiv.org/abs/2010.02372
  - title: 'Robust Federated Learning: The Case of Affine Distribution Shifts'
    affiliation: UC Santa Barbara; MIT
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/f5e536083a438cec5b64a4954abc17f1-Abstract.html
      PDF: https://arxiv.org/abs/2006.08907
      CODE: https://github.com/farzanfarnia/RobustFL
  - title: An Efficient Framework for Clustered Federated Learning
    affiliation: UC Berkeley; DeepMind
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/e32cc80bf07915058ce90722ee17bb71-Abstract.html
      PDF: https://arxiv.org/abs/2006.04088
      CODE: https://github.com/jichan3751/ifca
  - title: 'Distributionally Robust Federated Averaging :fire:'
    affiliation: Pennsylvania State University
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/ac450d10e166657ec8f93a1b65ca1b14-Abstract.html
      PDF: https://arxiv.org/abs/2102.12660
      CODE: https://github.com/MLOPTPSU/FedTorch
  - title: 'Personalized Federated Learning with Moreau Envelopes :fire:'
    affiliation: The University of Sydney
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/f4f1f13c8289ac1b1ee0ff176b56fc60-Abstract.html
      PDF: https://arxiv.org/abs/2006.08848
      CODE: https://github.com/CharlieDinh/pFedMe
  - title: 'Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic
      Meta-Learning Approach'
    affiliation: MIT; UT Austin
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/24389bfe4fe2eba8bf9aa9203a44cdad-Abstract.html
      PDF: https://arxiv.org/abs/2002.07948
      UC.: https://github.com/KarhouTam/Per-FedAvg
  - title: 'Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge'
    affiliation: USC
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/a1d4c20b182ad7137ab3606f0e3fc8a4-Abstract.html
      PDF: https://arxiv.org/abs/2007.14513
      CODE: https://github.com/FedML-AI/FedML/tree/master/fedml_experiments/distributed/fedgkt
      解读: https://zhuanlan.zhihu.com/p/536901871
  - title: 'Tackling the Objective Inconsistency Problem in Heterogeneous Federated
      Optimization :fire:'
    affiliation: CMU; Princeton
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/564127c03caab942e503ee6f810f54fd-Abstract.html
      PDF: https://arxiv.org/abs/2007.07481
      CODE: https://github.com/JYWa/FedNova
      UC.: https://github.com/carbonati/fl-zoo
  - title: 'Attack of the Tails: Yes, You Really Can Backdoor Federated Learning'
    affiliation: University of Wisconsin-Madison
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/b8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html
      PDF: https://arxiv.org/abs/2007.05084
  - title: Federated Accelerated Stochastic Gradient Descent
    affiliation: Stanford
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/39d0a8908fbe6c18039ea8227f827023-Abstract.html
      PDF: https://arxiv.org/abs/2006.08950
      CODE: https://github.com/hongliny/FedAc-NeurIPS20
      VIDEO: https://youtu.be/K28zpAgg3HM
  - title: 'Inverting Gradients - How easy is it to break privacy in federated learning?
      :fire:'
    affiliation: University of Siegen
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/c4ede56bbd98819ae6112b20ac6bf145-Abstract.html
      PDF: https://arxiv.org/abs/2003.14053
      CODE: https://github.com/JonasGeiping/invertinggradients
  - title: Ensemble Distillation for Robust Model Fusion in Federated Learning
    affiliation: EPFL
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/18df51b97ccd68128e994804f3eccc87-Abstract.html
      PDF: https://arxiv.org/abs/2006.07242
      CODE: https://github.com/epfml/federated-learning-public-code/tree/master/codes/FedDF-code
  - title: Throughput-Optimal Topology Design for Cross-Silo Federated Learning
    affiliation: INRIA
    venue: NeurIPS
    year: '2020'
    materials:
      PUB: https://proceedings.neurips.cc/paper/2020/hash/e29b722e35040b88678e25a1ec032a21-Abstract.html
      PDF: https://arxiv.org/abs/2010.12229
      CODE: https://github.com/omarfoq/communication-in-cross-silo-fl
  - title: 'Bayesian Nonparametric Federated Learning of Neural Networks :fire:'
    affiliation: IBM
    venue: ICML
    year: '2019'
    materials:
      PUB: http://proceedings.mlr.press/v97/yurochkin19a.html
      PDF: https://arxiv.org/abs/1905.12022
      CODE: https://github.com/IBM/probabilistic-federated-neural-matching
  - title: 'Analyzing Federated Learning through an Adversarial Lens :fire:'
    affiliation: Princeton; IBM
    venue: ICML
    year: '2019'
    materials:
      PUB: http://proceedings.mlr.press/v97/bhagoji19a.html
      PDF: https://arxiv.org/abs/1811.12470
      CODE: https://github.com/inspire-group/ModelPoisoning
  - title: Agnostic Federated Learning
    affiliation: Google
    venue: ICML
    year: '2019'
    materials:
      PUB: http://proceedings.mlr.press/v97/mohri19a.html
      PDF: https://arxiv.org/abs/1902.00146
  - title: 'cpSGD: Communication-efficient and differentially-private distributed
      SGD'
    affiliation: Princeton; Google
    venue: NeurIPS
    year: '2018'
    materials:
      PUB: https://papers.nips.cc/paper/2018/hash/21ce689121e39821d07d04faab328370-Abstract.html
      PDF: https://arxiv.org/abs/1805.10559
  - title: 'Federated Multi-Task Learning :fire:'
    affiliation: Stanford; USC; CMU
    venue: NeurIPS
    year: '2017'
    materials:
      PUB: https://papers.nips.cc/paper/2017/hash/6211080fa89981f66b1a0c9d55c61d0f-Abstract.html
      PDF: https://arxiv.org/abs/1705.10467
      CODE: https://github.com/gingsmith/fmtl
fl-in-top-dm-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                   '
    venue: '    Venue                     '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 58
    venue: 22
    year: 4
    materials: 60
  body:
  - title: Federated Unlearning for On-Device Recommendation
    affiliation: UQ
    venue: WSDM
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3539597.3570463
      PDF: https://arxiv.org/abs/2210.10958
  - title: Collaboration Equilibrium in Federated Learning
    affiliation: THU
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539237
      PDF: https://arxiv.org/abs/2108.07926
      CODE: https://github.com/cuis15/learning-to-collaborate
  - title: Connected Low-Loss Subspace Learning for a Personalization in Federated
      Learning
    affiliation: Ulsan National Institute of Science and Technology
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539254
      PDF: https://arxiv.org/abs/2109.07628
      CODE: https://github.com/vaseline555/superfed
  - title: 'FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal
      Split Networks'
    affiliation: University of Virginia
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539384
  - title: Communication-Efficient Robust Federated Learning with Noisy Labels
    affiliation: University of Pittsburgh
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539328
      PDF: https://arxiv.org/abs/2206.05558
  - title: 'FLDetector: Detecting Malicious Clients in Federated Learning via Checking
      Model-Updates Consistency'
    affiliation: USTC
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539231
      PDF: https://arxiv.org/abs/2207.09209
      CODE: https://github.com/zaixizhang/FLDetector
  - title: Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale
      Data
    affiliation: HKUST
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539402
      PDF: https://arxiv.org/abs/2105.08925
      CODE: https://github.com/Di-Chai/FedEval
  - title: 'FedWalk: Communication Efficient Federated Unsupervised Node Embedding
      with Differential Privacy'
    affiliation: SJTU
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539308
      PDF: https://arxiv.org/abs/2205.15896
  - title: 'FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform
      for Federated Graph Learning :fire:'
    affiliation: Alibaba
    venue: KDD (Best Paper Award)
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539112
      PDF: https://arxiv.org/abs/2204.05562
      CODE: https://github.com/alibaba/FederatedScope
  - title: 'Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to
      Dispatch'
    affiliation: BUAA
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539047
      PDF: https://hufudb.com/static/paper/2022/SIGKDD2022_Fed-LTD%20Towards%20Cross-Platform%20Ride%20Hailing%20via.pdf
      解读: https://zhuanlan.zhihu.com/p/544183874
  - title: 'Felicitas: Federated Learning in Distributed Cross Device Collaborative
      Frameworks'
    affiliation: USTC
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539039
      PDF: https://arxiv.org/abs/2202.08036
  - title: 'No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices'
    affiliation: Renmin University of China
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539086
      PDF: https://arxiv.org/abs/2202.08036
  - title: 'FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation
      via Hard Sampling'
    affiliation: THU
    venue: KDD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3534678.3539119
      PDF: https://arxiv.org/abs/2202.04975
      CODE: https://github.com/wuch15/FedAttack
  - title: 'PipAttack: Poisoning Federated Recommender Systems for Manipulating Item
      Promotion'
    affiliation: The University of Queensland
    venue: WSDM
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3488560.3498386
      PDF: https://arxiv.org/abs/2110.10926
  - title: 'Fed2: Feature-Aligned Federated Learning'
    affiliation: George Mason University; Microsoft; University of Maryland
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467309
      PDF: https://arxiv.org/abs/2111.14248
  - title: 'FedRS: Federated Learning with Restricted Softmax for Label Distribution
      Non-IID Data'
    affiliation: Nanjing University
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467254
      CODE: https://github.com/lxcnju/FedRepo
  - title: Federated Adversarial Debiasing for Fair and Trasnferable Representations
    affiliation: Michigan State University
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467281
      PAGE: https://jyhong.gitlab.io/publication/fade2021kdd/
      CODE: https://github.com/illidanlab/FADE
      SLIDE: https://jyhong.gitlab.io/publication/fade2021kdd/slides.pdf
  - title: Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
    affiliation: USC
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/pdf/10.1145/3447548.3467371
      CODE: https://github.com/mengcz13/KDD2021_CNFGNN
      解读: https://zhuanlan.zhihu.com/p/434839878
  - title: 'AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation
      Resource Utilization'
    affiliation: Xidian University;JD Tech
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467169
      PDF: https://arxiv.org/abs/2109.12519
  - title: 'FLOP: Federated Learning on Medical Datasets using Partial Networks'
    affiliation: Duke University
    venue: KDD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447548.3467185
      PDF: https://arxiv.org/abs/2102.05218.pdf
      CODE: https://github.com/jianyizhang123/FLOP
  - title: A Practical Federated Learning Framework for Small Number of Stakeholders
    affiliation: ETH Zürich
    venue: WSDM
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3437963.3441702
      CODE: https://github.com/MTC-ETH/Federated-Learning-source
  - title: Federated Deep Knowledge Tracing
    affiliation: USTC
    venue: WSDM
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3437963.3441747
      CODE: https://github.com/hxwujinze/federated-deep-knowledge-tracing
  - title: 'FedFast: Going Beyond Average for Faster Training of Federated Recommender
      Systems'
    affiliation: University College Dublin
    venue: KDD
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3394486.3403176
      VIDEO: https://papertalk.org/papertalks/23422
  - title: Federated Doubly Stochastic Kernel Learning for Vertically Partitioned
      Data
    affiliation: JD Tech
    venue: KDD
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3394486.3403298
      PDF: https://arxiv.org/abs/2008.06197
      VIDEO: https://papertalk.org/papertalks/23301
  - title: Federated Online Learning to Rank with Evolution Strategies
    affiliation: Facebook AI Research
    venue: WSDM
    year: '2019'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3289600.3290968
      CODE: http://github.com/facebookresearch/foltr-es
fl-in-top-secure-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                     '
    venue: '    Venue    '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 60
    venue: 5
    year: 4
    materials: 60
  body:
  - title: Securing Federated Sensitive Topic Classification against Poisoning Attacks
    affiliation: IMDEA Networks Institute
    venue: NDSS
    year: '2023'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/securing-federated-sensitive-topic-classification-against-poisoning-attacks/
      PDF: https://arxiv.org/abs/2201.13086
      CODE: https://github.com/FRM-Sec/FRM
  - title: 'PPA: Preference Profiling Attack Against Federated Learning'
    affiliation: NJUST
    venue: NDSS
    year: '2023'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/ppa-preference-profiling-attack-against-federated-learning/
      PDF: https://arxiv.org/abs/2202.04856
  - title: 'CERBERUS: Exploring Federated Prediction of Security Events'
    affiliation: UCL London
    venue: CCS
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3548606.3560580
      PDF: https://arxiv.org/abs/2209.03050
  - title: 'EIFFeL: Ensuring Integrity for Federated Learning'
    affiliation: UW-Madison
    venue: CCS
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3548606.3560611
      PDF: https://arxiv.org/abs/2112.12727
  - title: Eluding Secure Aggregation in Federated Learning via Model Inconsistency
    affiliation: SPRING Lab; EPFL
    venue: CCS
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3548606.3560557
      PDF: https://arxiv.org/abs/2111.07380
      CODE: https://github.com/pasquini-dario/eludingsecureaggregation
  - title: Federated Boosted Decision Trees with Differential Privacy
    affiliation: University of Warwick
    venue: CCS
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3548606.3560687
      PDF: https://arxiv.org/abs/2210.02910
      CODE: https://github.com/Samuel-Maddock/federated-boosted-dp-trees
  - title: 'FedRecover: Recovering from Poisoning Attacks in Federated Learning using
      Historical Information'
    affiliation: Duke University
    venue: S&P
    year: '2023'
    materials:
      PUB: https://www.computer.org/csdl/proceedings-article/sp/2023/933600a326/1He7Y3q8FMY
      PDF: https://arxiv.org/abs/2210.10936
  - title: 'Private, Efficient, and Accurate: Protecting Models Trained by Multi-party
      Learning with Differential Privacy'
    affiliation: Fudan University
    venue: S&P
    year: '2023'
    materials:
      PUB: https://www.computer.org/csdl/proceedings-article/sp/2023/933600a076/1He7XMLcnsc
      PDF: https://arxiv.org/abs/2208.08662
  - title: 'Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks
      on Production Federated Learning'
    affiliation: University of Massachusetts
    venue: S&P
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9833647/
      VIDEO: https://www.youtube.com/watch?v=tQv3CpxIyvs
  - title: 'SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost'
    affiliation: Microsoft Research
    venue: USENIX Security
    year: '2022'
    materials:
      PUB: https://www.usenix.org/conference/usenixsecurity22/presentation/chandran
      PDF: https://eprint.iacr.org/2021/1538
      CODE: https://github.com/shahakash28/simc
      VIDEO: https://www.youtube.com/watch?v=0Oaqi0JHUac
      SUPP: https://www.usenix.org/system/files/usenixsecurity22-chandran.pdf
  - title: Efficient Differentially Private Secure Aggregation for Federated Learning
      via Hardness of Learning with Errors
    affiliation: University of Vermont
    venue: USENIX Security
    year: '2022'
    materials:
      PUB: https://www.usenix.org/conference/usenixsecurity22/presentation/stevens
      SLIDE: https://www.usenix.org/system/files/sec22_slides-stevens.pdf
      VIDEO: https://www.youtube.com/watch?v=9kYHQkr6DuE
  - title: Label Inference Attacks Against Vertical Federated Learning
    affiliation: ZJU
    venue: USENIX Security
    year: '2022'
    materials:
      PUB: https://www.usenix.org/conference/usenixsecurity22/presentation/fu-chong
      SLIDE: https://www.usenix.org/system/files/sec22_slides-fu-chong.pdf
      CODE: https://github.com/FuChong-cyber/label-inference-attacks
      VIDEO: https://www.youtube.com/watch?v=JEmRbDtosVw
  - title: 'FLAME: Taming Backdoors in Federated Learning'
    affiliation: Technical University of Darmstadt
    venue: USENIX Security
    year: '2022'
    materials:
      PUB: https://www.usenix.org/conference/usenixsecurity22/presentation/nguyen
      SLIDE: https://www.usenix.org/system/files/sec22_slides-nguyen.pdf
      PDF: https://arxiv.org/abs/2101.02281
      VIDEO: https://www.youtube.com/watch?v=nMrte2S9U68
  - title: Local and Central Differential Privacy for Robustness and Privacy in Federated
      Learning
    affiliation: University at Buffalo, SUNY
    venue: NDSS
    year: '2022'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/auto-draft-204/
      PDF: https://arxiv.org/abs/2009.03561
      VIDEO: https://www.youtube.com/watch?v=_aH2j5A3608&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=3
      UC.: https://github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning
  - title: Interpretable Federated Transformer Log Learning for Cloud Threat Forensics
    affiliation: University of the Incarnate Word
    venue: NDSS
    year: '2022'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/auto-draft-236/
      VIDEO: https://www.youtube.com/watch?v=3HoysA6hsC8&list=PLfUWWM-POgQsS08uHJUJI6sawDO_3sNh0&index=3
      UC.: https://github.com/cyberthreat-datasets/ctdd-2021-os-syslogs
  - title: 'FedCRI: Federated Mobile Cyber-Risk Intelligence'
    affiliation: Technical University of Darmstadt
    venue: NDSS
    year: '2022'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/auto-draft-229/
      VIDEO: https://www.youtube.com/watch?v=2zmdPqCCFxg&list=PLfUWWM-POgQs8ZZMMCX1RoNnmSQ70QXxd&index=3
  - title: 'DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep
      Model Inspection'
    affiliation: Technical University of Darmstadt
    venue: NDSS
    year: '2022'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/auto-draft-205/
      PDF: https://arxiv.org/abs/2201.00763
      VIDEO: https://www.youtube.com/watch?v=MJF_7vnoGh4&list=PLfUWWM-POgQulyX2vzKzUtZEkVn1M9G2a&index=4
  - title: Private Hierarchical Clustering in Federated Networks
    affiliation: NUS
    venue: CCS
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3460120.3484822
      PDF: https://arxiv.org/abs/2105.09057
  - title: 'FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping'
    affiliation: Duke University
    venue: NDSS
    year: '2021'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/fltrust-byzantine-robust-federated-learning-via-trust-bootstrapping/
      PDF: https://arxiv.org/abs/2012.13995
      CODE: https://people.duke.edu/~zg70/code/fltrust.zip
      VIDEO: https://www.youtube.com/watch?v=zhhdPgKPCN0&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=2
      SLIDE: https://people.duke.edu/~zg70/code/Secure_Federated_Learning.pdf
  - title: 'POSEIDON: Privacy-Preserving Federated Neural Network Learning'
    affiliation: EPFL
    venue: NDSS
    year: '2021'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/poseidon-privacy-preserving-federated-neural-network-learning/
      VIDEO: https://www.youtube.com/watch?v=kX6-PMzxZ3c&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=1
  - title: 'Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses
      for Federated Learning'
    affiliation: University of Massachusetts Amherst
    venue: NDSS
    year: '2021'
    materials:
      PUB: https://www.ndss-symposium.org/ndss-paper/manipulating-the-byzantine-optimizing-model-poisoning-attacks-and-defenses-for-federated-learning/
      CODE: https://github.com/vrt1shjwlkr/NDSS21-Model-Poisoning
      VIDEO: https://www.youtube.com/watch?v=G2VYRnLqAXE&list=PLfUWWM-POgQvaqlGPwlOa0JR3bryB1KCS&index=3
  - title: Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
    affiliation: The Ohio State University
    venue: USENIX Security
    year: '2020'
    materials:
      PUB: https://www.usenix.org/conference/usenixsecurity20/presentation/fang
      PDF: https://arxiv.org/abs/1911.11815
      CODE: https://people.duke.edu/~zg70/code/fltrust.zip
      VIDEO: https://www.youtube.com/watch?v=SQ12UpYrUVU&feature=emb_imp_woyt
      SLIDE: https://www.usenix.org/system/files/sec20_slides_fang.pdf
  - title: A Reliable and Accountable Privacy-Preserving Federated Learning Framework
      using the Blockchain
    affiliation: University of Kansas
    venue: CCS (Poster)
    year: '2019'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3319535.3363256
  - title: 'IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing
      Federated Learning'
    affiliation: Université du Québéc á Montréal
    venue: S&P （Workshop）
    year: '2019'
    materials:
      PUB: https://ieeexplore.ieee.org/document/8844592
  - title: 'Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box
      Inference Attacks against Centralized and Federated Learning :fire:'
    affiliation: University of Massachusetts Amherst
    venue: S&P
    year: '2019'
    materials:
      PUB: https://www.computer.org/csdl/proceedings-article/sp/2019/666000a739/1dlwhtj4r7O
      VIDEO: https://youtu.be/lzJY4BjCxTc
      SLIDE: https://www.ieee-security.org/TC/SP2019/SP19-Slides-pdfs/Milad_Nasr_-_08-Milad_Nasr-Comprehensive_Privacy_Analysis_of_Deep_Learning_
      CODE: https://github.com/privacytrustlab/ml_privacy_meter
  - title: Practical Secure Aggregation for Privacy Preserving Machine Learning
    affiliation: Google
    venue: CCS
    year: '2017'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3133956.3133982
      PDF: https://eprint.iacr.org/2017/281
      解读: https://zhuanlan.zhihu.com/p/445656765
      UC.: https://github.com/Chen-Junbao/SecureAggregation
      UC: https://github.com/corentingiraud/federated-learning-secure-aggregation
fl-in-top-cv-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                     '
    venue: '    Venue    '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 60
    venue: 5
    year: 4
    materials: 60
  body:
  - title: 'Rethinking Federated Learning With Domain Shift: A Prototype View'
    affiliation: WHU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html
      CODE: https://github.com/WenkeHuang/RethinkFL
  - title: Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning
    affiliation: ECNU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html
      CODE: https://github.com/minglllli/CBAFed
  - title: 'DaFKD: Domain-Aware Federated Knowledge Distillation'
    affiliation: HUST
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html
      CODE: https://github.com/haozhaowang/DaFKD2023
  - title: The Resource Problem of Using Linear Layer Leakage Attack in Federated
      Learning
    affiliation: Purdue University
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.14868
  - title: 'FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation'
    affiliation: ZJU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Miao_FedSeg_Class-Heterogeneous_Federated_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html
  - title: On the Effectiveness of Partial Variance Reduction in Federated Learning
      With Heterogeneous Data
    affiliation: DTU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Li_On_the_Effectiveness_of_Partial_Variance_Reduction_in_Federated_Learning_CVPR_2023_paper.html
      PDF: https://arxiv.org/abs/2212.02191
  - title: Elastic Aggregation for Federated Optimization
    affiliation: Meituan
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Elastic_Aggregation_for_Federated_Optimization_CVPR_2023_paper.html
  - title: 'FedDM: Iterative Distribution Matching for Communication-Efficient Federated
      Learning'
    affiliation: UCLA
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_FedDM_Iterative_Distribution_Matching_for_Communication-Efficient_Federated_Learning_CVPR_2023_paper.html
      PDF: https://arxiv.org/abs/2207.09653
  - title: Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
    affiliation: UM
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Liao_Adaptive_Channel_Sparsity_for_Federated_Learning_Under_System_Heterogeneity_CVPR_2023_paper.html
  - title: 'ScaleFL: Resource-Adaptive Federated Learning With Heterogeneous Clients'
    affiliation: GaTech
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Ilhan_ScaleFL_Resource-Adaptive_Federated_Learning_With_Heterogeneous_Clients_CVPR_2023_paper.html
      CODE: https://github.com/git-disl/scale-fl
  - title: Reliable and Interpretable Personalized Federated Learning
    affiliation: TJU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Reliable_and_Interpretable_Personalized_Federated_Learning_CVPR_2023_paper.html
  - title: Federated Domain Generalization With Generalization Adjustment
    affiliation: SJTU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Federated_Domain_Generalization_With_Generalization_Adjustment_CVPR_2023_paper.html
      CODE: https://github.com/MediaBrain-SJTU/FedDG-GA
  - title: Make Landscape Flatter in Differentially Private Federated Learning
    affiliation: THU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Shi_Make_Landscape_Flatter_in_Differentially_Private_Federated_Learning_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.11242
      CODE: https://github.com/YMJS-Irfan/DP-FedSAM
  - title: Confidence-Aware Personalized Federated Learning via Variational Expectation
      Maximization
    affiliation: KU Leuven
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Confidence-Aware_Personalized_Federated_Learning_via_Variational_Expectation_Maximization_CVPR_2023_paper.html
      PDF: https://arxiv.org/abs/2305.12557
      CODE: https://github.com/junyizhu-ai/confidence_aware_pfl
  - title: 'STDLens: Model Hijacking-Resilient Federated Learning for Object Detection'
    affiliation: GaTech
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Chow_STDLens_Model_Hijacking-Resilient_Federated_Learning_for_Object_Detection_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.11511
      CODE: https://github.com/git-disl/STDLens
  - title: Re-Thinking Federated Active Learning Based on Inter-Class Diversity
    affiliation: KAIST
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Re-Thinking_Federated_Active_Learning_Based_on_Inter-Class_Diversity_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.12317
      CODE: https://github.com/raymin0223/LoGo
  - title: Learning Federated Visual Prompt in Null Space for MRI Reconstruction
    affiliation: A*STAR
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Learning_Federated_Visual_Prompt_in_Null_Space_for_MRI_Reconstruction_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.16181
      CODE: https://github.com/chunmeifeng/FedPR
  - title: Fair Federated Medical Image Segmentation via Client Contribution Estimation
    affiliation: CUHK
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Fair_Federated_Medical_Image_Segmentation_via_Client_Contribution_Estimation_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2303.16520
      CODE: https://github.com/NVIDIA/NVFlare/tree/dev/research/fed-ce
  - title: Federated Learning With Data-Agnostic Distribution Fusion
    affiliation: NJU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Duan_Federated_Learning_With_Data-Agnostic_Distribution_Fusion_CVPR_2023_paper.html
      CODE: https://github.com/LiruichenSpace/FedFusion
  - title: How To Prevent the Poor Performance Clients for Personalized Federated
      Learning?
    affiliation: CSU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Qu_How_To_Prevent_the_Poor_Performance_Clients_for_Personalized_Federated_CVPR_2023_paper.html
  - title: 'GradMA: A Gradient-Memory-Based Accelerated Federated Learning With Alleviated
      Catastrophic Forgetting'
    affiliation: ECNU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GradMA_A_Gradient-Memory-Based_Accelerated_Federated_Learning_With_Alleviated_Catastrophic_Forgetting_CVPR_2023_paper.html
      PDF: http://arxiv.org/abs/2302.14307
      CODE: https://github.com/lkyddd/gradma
  - title: Bias-Eliminating Augmentation Learning for Debiased Federated Learning
    affiliation: NTU
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Bias-Eliminating_Augmentation_Learning_for_Debiased_Federated_Learning_CVPR_2023_paper.html
  - title: Federated Incremental Semantic Segmentation
    affiliation: CAS; UCAS
    venue: CVPR
    year: '2023'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Federated_Incremental_Semantic_Segmentation_CVPR_2023_paper.html
      PDF: https://arxiv.org/abs/2304.04620
      CODE: https://github.com/JiahuaDong/FISS
  - title: 'Confederated Learning: Going Beyond Centralization'
    affiliation: CAS;  UCAS
    venue: MM
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3503161.3548157
  - title: Few-Shot Model Agnostic Federated Learning
    affiliation: WHU
    venue: MM
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3503161.3548764
      CODE: https://github.com/WenkeHuang/FSMAFL
  - title: 'Feeling Without Sharing: A Federated Video Emotion Recognition Framework
      Via Privacy-Agnostic Hybrid Aggregation'
    affiliation: TJUT
    venue: MM
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3503161.3548278
  - title: 'FedLTN: Federated Learning for Sparse and Personalized Lottery Ticket
      Networks'
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/6634_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136720069-supp.pdf
  - title: 'Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional
      Medical Image Segmentation'
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/1129_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136810431-supp.pdf
      PDF: https://arxiv.org/abs/2203.06338
      CODE: https://github.com/guopengf/Auto-FedRL
  - title: Improving Generalization in Federated Learning by Seeking Flat Minima
    affiliation: Politecnico di Torino
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/7093_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136830636-supp.pdf
      PDF: https://arxiv.org/abs/2203.11834
      CODE: https://github.com/debcaldarola/fedsam
  - title: 'AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias
      Estimation'
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/8092_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136830690-supp.pdf
      PDF: https://arxiv.org/abs/2204.13170
      CODE: https://github.com/varnio/fedsim
      PAGE: https://fedsim.varnio.com/en/latest/
  - title: 'SphereFed: Hyperspherical Federated Learning'
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/2255_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136860161-supp.pdf
      PDF: https://arxiv.org/abs/2207.09413
  - title: Federated Self-Supervised Learning for Video Understanding
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/7693_ECCV_2022_paper.php
      PDF: https://arxiv.org/abs/2207.01975
      CODE: https://github.com/yasar-rehman/fedvssl
  - title: 'FedVLN: Privacy-Preserving Federated Vision-and-Language Navigation'
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/6298_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136960673-supp.pdf
      PDF: https://arxiv.org/abs/2203.14936
      CODE: https://github.com/eric-ai-lab/FedVLN
  - title: Addressing Heterogeneity in Federated Learning via Distributional Transformation
    affiliation: ''
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/6551_ECCV_2022_paper.php
      CODE: https://github.com/hyhmia/DisTrans
  - title: 'FedX: Unsupervised Federated Learning with Cross Knowledge Distillation'
    affiliation: KAIST
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/3932_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136900682-supp.pdf
      PDF: https://arxiv.org/abs/2207.09158
      CODE: https://github.com/sungwon-han/fedx
  - title: Personalizing Federated Medical Image Segmentation via Local Calibration
    affiliation: Xiamen University
    venue: ECCV
    year: '2022'
    materials:
      PUB: https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/1626_ECCV_2022_paper.php
      SUPP: https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136810449-supp.pdf
      PDF: https://arxiv.org/abs/2207.04655
      CODE: https://github.com/jcwang123/fedlc
  - title: 'ATPFL: Automatic Trajectory Prediction Model Design Under Federated Learning
      Framework'
    affiliation: HIT
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Wang_ATPFL_Automatic_Trajectory_Prediction_Model_Design_Under_Federated_Learning_Framework_CVPR_2022_paper.html
  - title: Rethinking Architecture Design for Tackling Data Heterogeneity in Federated
      Learning
    affiliation: Stanford
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Qu_Rethinking_Architecture_Design_for_Tackling_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Qu_Rethinking_Architecture_Design_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2106.06047
      CODE: https://github.com/Liangqiong/ViT-FL-main
      VIDEO: https://www.youtube.com/watch?v=Ae1CDi0_Nok&ab_channel=StanfordMedAI
  - title: 'FedCorr: Multi-Stage Federated Learning for Label Noise Correction'
    affiliation: Singapore University of Technology and Design
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Xu_FedCorr_Multi-Stage_Federated_Learning_for_Label_Noise_Correction_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Xu_FedCorr_Multi-Stage_Federated_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2204.04677
      CODE: https://github.com/xu-jingyi/fedcorr
      VIDEO: https://www.youtube.com/watch?v=GA22ct1LgRA&ab_channel=ZihanChen
  - title: 'FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous
      Federated Learning'
    affiliation: Duke University
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Tang_FedCor_Correlation-Based_Active_Client_Selection_Strategy_for_Heterogeneous_Federated_Learning_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Tang_FedCor_Correlation-Based_Active_CVPR_2022_supplemental.zip
      PDF: http://arxiv.org/abs/2103.13822
  - title: Layer-Wised Model Aggregation for Personalized Federated Learning
    affiliation: PolyU
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Ma_Layer-Wised_Model_Aggregation_for_Personalized_Federated_Learning_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Ma_Layer-Wised_Model_Aggregation_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2205.03993
  - title: 'Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning'
    affiliation: University of Central Florida
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Mendieta_Local_Learning_Matters_Rethinking_Data_Heterogeneity_in_Federated_Learning_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Mendieta_Local_Learning_Matters_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2111.14213
      CODE: https://github.com/mmendiet/FedAlign
  - title: Federated Learning With Position-Aware Neurons
    affiliation: Nanjing University
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Li_Federated_Learning_With_Position-Aware_Neurons_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_Federated_Learning_With_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2203.14666
  - title: 'RSCFed: Random Sampling Consensus Federated Semi-Supervised Learning'
    affiliation: HKUST
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Liang_RSCFed_Random_Sampling_Consensus_Federated_Semi-Supervised_Learning_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Liang_RSCFed_Random_Sampling_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2203.13993
      CODE: https://github.com/xmed-lab/rscfed
  - title: Learn From Others and Be Yourself in Heterogeneous Federated Learning
    affiliation: Wuhan University
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Huang_Learn_From_Others_and_Be_Yourself_in_Heterogeneous_Federated_Learning_CVPR_2022_paper.html
      CODE: https://github.com/wenkehuang/fccl
      VIDEO: https://www.youtube.com/watch?v=zZoASA71qwQ&ab_channel=HuangWenke
  - title: Robust Federated Learning With Noisy and Heterogeneous Clients
    affiliation: Wuhan University
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Fang_Robust_Federated_Learning_With_Noisy_and_Heterogeneous_Clients_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Fang_Robust_Federated_Learning_CVPR_2022_supplemental.pdf
      CODE: https://github.com/FangXiuwen/Robust_FL
  - title: 'ResSFL: A Resistance Transfer Framework for Defending Model Inversion
      Attack in Split Federated Learning'
    affiliation: Arizona State University
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Li_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html
      SUPP: https://openaccess.thecvf.com/content/CVPR2022/supplemental/Li_ResSFL_A_Resistance_CVPR_2022_supplemental.pdf
      PDF: http://arxiv.org/abs/2205.04007
      CODE: https://github.com/zlijingtao/ResSFL
  - title: 'FedDC: Federated Learning With Non-IID Data via Local Drift Decoupling
      and Correction'
    affiliation: National University of Defense Technology
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Gao_FedDC_Federated_Learning_With_Non-IID_Data_via_Local_Drift_Decoupling_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.11751
      CODE: https://github.com/gaoliang13/FedDC
      解读: https://zhuanlan.zhihu.com/p/505889549
  - title: Federated Class-Incremental Learning
    affiliation: CAS; Northwestern University; UTS
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Dong_Federated_Class-Incremental_Learning_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.11473
      CODE: https://github.com/conditionWang/FCIL
  - title: Fine-Tuning Global Model via Data-Free Knowledge Distillation for Non-IID
      Federated Learning
    affiliation: PKU;  JD Explore Academy;  The University of Sydney
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Zhang_Fine-Tuning_Global_Model_via_Data-Free_Knowledge_Distillation_for_Non-IID_Federated_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.09249
  - title: Differentially Private Federated Learning With Local Regularization and
      Sparsification
    affiliation: CAS
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Cheng_Differentially_Private_Federated_Learning_With_Local_Regularization_and_Sparsification_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.03106
  - title: Auditing Privacy Defenses in Federated Learning via Generative Gradient
      Leakage
    affiliation: University of Tennessee; Oak Ridge National Laboratory; Google Research
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Li_Auditing_Privacy_Defenses_in_Federated_Learning_via_Generative_Gradient_Leakage_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.15696
      CODE: https://github.com/zhuohangli/GGL
      VIDEO: https://www.youtube.com/watch?v=rphFSGDlGPY&ab_channel=MoSISLab
  - title: 'CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization
      in Federated Learning'
    affiliation: SJTU
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Shen_CD2-pFed_Cyclic_Distillation-Guided_Channel_Decoupling_for_Model_Personalization_in_Federated_CVPR_2022_paper.html
      PDF: https://arxiv.org/abs/2204.03880
  - title: Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation
    affiliation: Univ. of Pittsburgh; NVIDIA
    venue: CVPR
    year: '2022'
    materials:
      PUB: https://openaccess.thecvf.com/content/CVPR2022/html/Xu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.html
      PDF: http://arxiv.org/abs/2203.10144
  - title: Multi-Institutional Collaborations for Improving Deep Learning-Based Magnetic
      Resonance Image Reconstruction Using Federated Learning
    affiliation: Johns Hopkins University
    venue: CVPR
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9578476
      PDF: https://arxiv.org/abs/2103.02148
      CODE: https://github.com/guopengf/FL-MRCM
  - title: 'Model-Contrastive Federated Learning :fire:'
    affiliation: NUS; UC Berkeley
    venue: CVPR
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9578660
      PDF: https://arxiv.org/abs/2103.16257
      CODE: https://github.com/QinbinLi/MOON
      解读: https://weisenhui.top/posts/17666.html
  - title: 'FedDG: Federated Domain Generalization on Medical Image Segmentation via
      Episodic Learning in Continuous Frequency Space :fire:'
    affiliation: CUHK
    venue: CVPR
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9577482
      PDF: https://arxiv.org/abs/2103.06030
      CODE: https://github.com/liuquande/FedDG-ELCFS
  - title: 'Soteria: Provable Defense Against Privacy Leakage in Federated Learning
      From Representation Perspective'
    affiliation: Duke University
    venue: CVPR
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9578192
      PDF: https://arxiv.org/abs/2012.06043
      CODE: https://github.com/jeremy313/Soteria
  - title: Federated Learning for Non-IID Data via Unified Feature Learning and Optimization
      Objective Alignment
    affiliation: PKU
    venue: ICCV
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9710573
  - title: Ensemble Attention Distillation for Privacy-Preserving Federated Learning
    affiliation: University at Buffalo
    venue: ICCV
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9710586
      PDF: https://openaccess.thecvf.com/content/ICCV2021/papers/Gong_Ensemble_Attention_Distillation_for_Privacy-Preserving_Federated_Learning_ICCV_2021_paper.pdf
  - title: Collaborative Unsupervised Visual Representation Learning from Decentralized
      Data
    affiliation: NTU; SenseTime
    venue: ICCV
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9710366
      PDF: https://arxiv.org/abs/2108.06492
  - title: Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person
      Re-identification
    affiliation: NTU
    venue: MM
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3474085.3475182
      PDF: https://arxiv.org/abs/2108.06493
  - title: Federated Visual Classification with Real-World Data Distribution
    affiliation: MIT; Google
    venue: ECCV
    year: '2020'
    materials:
      PUB: https://link.springer.com/chapter/10.1007/978-3-030-58607-2_5
      PDF: https://arxiv.org/abs/2003.08082
      VIDEO: https://www.youtube.com/watch?v=Rc67rZzPDDY&ab_channel=TzuMingHsu
  - title: 'InvisibleFL: Federated Learning over Non-Informative Intermediate Updates
      against Multimedia Privacy Leakages'
    affiliation: ''
    venue: MM
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3394171.3413923
  - title: Performance Optimization of Federated Person Re-identification via Benchmark
      Analysis **`data.`**
    affiliation: NTU
    venue: MM
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3394171.3413814
      PDF: https://arxiv.org/abs/2008.11560
      CODE: https://github.com/cap-ntu/FedReID
      解读: https://zhuanlan.zhihu.com/p/265987079
fl-in-top-nlp-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                          '
    venue: '    Venue             '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 49
    venue: 14
    year: 4
    materials: 60
  body:
  - title: 'Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise
      Krum-Based Aggregation'
    affiliation: PKU
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.findings-emnlp.25/
      PDF: https://arxiv.org/abs/2210.06894
  - title: Efficient Federated Learning on Knowledge Graphs via Privacy-preserving
      Relation Embedding Aggregation **`kg.`**
    affiliation: Lehigh University
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.findings-emnlp.43/
      PDF: https://arxiv.org/abs/2203.09553
      CODE: https://github.com/taokz/FedR
  - title: Federated Continual Learning for Text Classification via Selective Inter-client
      Transfer
    affiliation: DRIMCo GmbH; LMU
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.findings-emnlp.353
      PDF: https://arxiv.org/abs/2210.06101
      CODE: https://github.com/raipranav/fcl-fedseit
  - title: Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient
      Ensembling
    affiliation: SNU
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.emnlp-main.6/
      PDF: https://arxiv.org/abs/2204.14017
  - title: A Federated Approach to Predicting Emojis in Hindi Tweets
    affiliation: University of Alberta
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.emnlp-main.819
      PDF: https://arxiv.org/abs/2211.06401
      CODE: https://github.com/deep1401/fedmoji
  - title: Federated Model Decomposition with Private Vocabulary for Text Classification
    affiliation: HIT; Peng Cheng Lab
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.emnlp-main.430
      CODE: https://github.com/SMILELab-FL/FedVocab
  - title: Federated Meta-Learning for Emotion and Sentiment Aware Multi-modal Complaint
      Identification
    affiliation: ''
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=rVgVJ9eWxM9
  - title: Fair NLP Models with Differentially Private Text Encoders
    affiliation: ''
    venue: EMNLP
    year: '2022'
    materials:
      PUB: https://openreview.net/forum?id=BVgNSki6q1c
      PDF: https://arxiv.org/abs/2205.06135
      CODE: https://github.com/saist1993/dpnlp
  - title: Scaling Language Model Size in Cross-Device Federated Learning
    affiliation: Google
    venue: ACL workshop
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.fl4nlp-1.2/
      PDF: https://arxiv.org/abs/2204.09715
  - title: Intrinsic Gradient Compression for Scalable and Efficient Federated Learning
    affiliation: Oxford
    venue: ACL workshop
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.fl4nlp-1.4/
      PDF: https://arxiv.org/abs/2112.02656
  - title: 'ActPerFL: Active Personalized Federated Learning'
    affiliation: Amazon
    venue: ACL workshop
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.fl4nlp-1.1
      PAGE: https://www.amazon.science/publications/actperfl-active-personalized-federated-learning
  - title: 'FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing
      Tasks :fire:'
    affiliation: USC
    venue: NAACL
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.findings-naacl.13/
      PDF: https://arxiv.org/abs/2104.08815
      CODE: https://github.com/FedML-AI/FedNLP
  - title: Federated Learning with Noisy User Feedback
    affiliation: USC; Amazon
    venue: NAACL
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.naacl-main.196/
      PDF: https://arxiv.org/abs/2205.03092
  - title: Training Mixed-Domain Translation Models via Federated Learning
    affiliation: Amazon
    venue: NAACL
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.naacl-main.186
      PAGE: https://www.amazon.science/publications/training-mixed-domain-translation-models-via-federated-learning
      PDF: https://arxiv.org/abs/2205.01557
  - title: Pretrained Models for Multilingual Federated Learning
    affiliation: Johns Hopkins University
    venue: NAACL
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.naacl-main.101
      PDF: https://arxiv.org/abs/2206.02291
      CODE: https://github.com/orionw/multilingual-federated-learning
  - title: Training Mixed-Domain Translation Models via Federated Learning
    affiliation: Amazon
    venue: NAACL
    year: '2022'
    materials:
      PUB: https://aclanthology.org/2022.naacl-main.186/
      PAGE: https://www.amazon.science/publications/training-mixed-domain-translation-models-via-federated-learning
      PDF: https://arxiv.org/abs/2205.01557
  - title: Federated Chinese Word Segmentation with Global Character Associations
    affiliation: University of Washington
    venue: ACL workshop
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.findings-acl.376
      CODE: https://github.com/cuhksz-nlp/GCASeg
  - title: 'Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving
      News Recommendation'
    affiliation: USTC
    venue: EMNLP
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.emnlp-main.223
      PDF: https://arxiv.org/abs/2109.05446
      CODE: https://github.com/yjw1029/Efficient-FedRec
      VIDEO: https://aclanthology.org/2021.emnlp-main.223.mp4
  - title: Improving Federated Learning for Aspect-based Sentiment Analysis via Topic
      Memories
    affiliation: CUHK (Shenzhen)
    venue: EMNLP
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.emnlp-main.321/
      CODE: https://github.com/cuhksz-nlp/ASA-TM
      VIDEO: https://aclanthology.org/2021.emnlp-main.321.mp4
  - title: A Secure and Efficient Federated Learning Framework for NLP
    affiliation: University of Connecticut
    venue: EMNLP
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.emnlp-main.606
      PDF: https://arxiv.org/abs/2201.11934
      VIDEO: https://aclanthology.org/2021.emnlp-main.606.mp4
  - title: Distantly Supervised Relation Extraction in Federated Settings
    affiliation: UCAS
    venue: EMNLP workshop
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.findings-emnlp.52
      PDF: https://arxiv.org/abs/2008.05049
      CODE: https://github.com/DianboWork/FedDS
  - title: Federated Learning with Noisy User Feedback
    affiliation: USC; Amazon
    venue: NAACL workshop
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2022.naacl-main.196
      PDF: https://arxiv.org/abs/2205.03092
  - title: An Investigation towards Differentially Private Sequence Tagging in a Federated
      Framework
    affiliation: Universität Hamburg
    venue: NAACL workshop
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.privatenlp-1.4
  - title: Understanding Unintended Memorization in Language Models Under Federated
      Learning
    affiliation: Google
    venue: NAACL workshop
    year: '2021'
    materials:
      PUB: https://aclanthology.org/2021.privatenlp-1.1
      PDF: https://arxiv.org/abs/2006.07490
  - title: 'FedED: Federated Learning via Ensemble Distillation for Medical Relation
      Extraction'
    affiliation: CAS
    venue: EMNLP
    year: '2020'
    materials:
      PUB: https://aclanthology.org/2020.emnlp-main.165
      VIDEO: https://slideslive.com/38939230
      解读: https://zhuanlan.zhihu.com/p/539347225
  - title: Empirical Studies of Institutional Federated Learning For Natural Language
      Processing
    affiliation: Ping An Technology
    venue: EMNLP workshop
    year: '2020'
    materials:
      PUB: https://aclanthology.org/2020.findings-emnlp.55
  - title: Federated Learning for Spoken Language Understanding
    affiliation: PKU
    venue: COLING
    year: '2020'
    materials:
      PUB: https://aclanthology.org/2020.coling-main.310/
  - title: Two-stage Federated Phenotyping and Patient Representation Learning
    affiliation: Boston Children’s Hospital Harvard Medical School
    venue: ACL workshop
    year: '2019'
    materials:
      PUB: https://aclanthology.org/W19-5030
      PDF: https://arxiv.org/abs/1908.05596
      CODE: https://github.com/kaiyuanmifen/FederatedNLP
      UC.: https://github.com/MarcioPorto/federated-phenotyping
fl-in-top-db-conference-and-journal:
  header:
    title: 'Title                                                        '
    affiliation: ' Affiliation                     '
    venue: ' Venue           '
    year: ' Year '
    materials: ' Materials'
  length:
    title: 60
    affiliation: 31
    venue: 15
    year: 4
    materials: 60
  body:
  - title: Differentially Private Vertical Federated Clustering.
    affiliation: Purdue University
    venue: VLDB
    year: '2023'
    materials:
      PUB: https://www.vldb.org/pvldb/vol16/p1277-li.pdf
      PDF: https://arxiv.org/abs/2208.01700
      CODE: https://anonymous.4open.science/r/public_vflclustering-63CD/README.md
  - title: 'FederatedScope: A Flexible Federated Learning Platform for Heterogeneity.
      :fire:'
    affiliation: Alibaba
    venue: VLDB
    year: '2023'
    materials:
      PUB: https://www.vldb.org/pvldb/vol16/p1059-li.pdf
      PDF: https://arxiv.org/abs/2204.05011
      CODE: https://github.com/alibaba/FederatedScope
  - title: Secure Shapley Value for Cross-Silo Federated Learning.
    affiliation: Kyoto University
    venue: VLDB
    year: '2023'
    materials:
      PUB: https://www.vldb.org/pvldb/vol16/p1657-zheng.pdf
      PDF: https://arxiv.org/abs/2209.04856
      CODE: https://github.com/teijyogen/secsv
  - title: 'OpBoost: A Vertical Federated Tree Boosting Framework Based on Order-Preserving
      Desensitization'
    affiliation: ZJU
    venue: VLDB
    year: '2022'
    materials:
      PUB: https://www.vldb.org/pvldb/vol16/p202-li.pdf
      PDF: https://arxiv.org/abs/2210.01318
      CODE: https://github.com/alibaba-edu/mpc4j/tree/main/mpc4j-sml-opboost
  - title: 'Skellam Mixture Mechanism: a Novel Approach to Federated Learning with
      Differential Privacy.'
    affiliation: NUS
    venue: VLDB
    year: '2022'
    materials:
      PUB: https://www.vldb.org/pvldb/vol15/p2348-bao.pdf
      CODE: https://github.com/SkellamMixtureMechanism/SMM
  - title: Towards Communication-efficient Vertical Federated Learning Training via
      Cache-enabled Local Update
    affiliation: PKU
    venue: VLDB
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.14778/3547305.3547316
      PDF: https://arxiv.org/abs/2207.14628
      CODE: https://github.com/ccchengff/FDL/tree/main/playground/celu_vfl
  - title: 'FedTSC: A Secure Federated Learning System for Interpretable Time Series
      Classification.'
    affiliation: HIT
    venue: VLDB
    year: '2022'
    materials:
      PUB: https://www.vldb.org/pvldb/vol15/p3686-wang.pdf
      CODE: https://github.com/hit-mdc/FedTSC-FedST
  - title: Improving Fairness for Data Valuation in Horizontal Federated Learning
    affiliation: The UBC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835382
      PDF: https://arxiv.org/abs/2109.09046
  - title: 'FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to
      System Heterogeneity'
    affiliation: USTC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835545
      PDF: https://arxiv.org/abs/2204.03529
      CODE: https://github.com/YonghaiGong/FedADMM
  - title: 'FedMP: Federated Learning through Adaptive Model Pruning in Heterogeneous
      Edge Computing.'
    affiliation: USTC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835327
  - title: 'Federated Learning on Non-IID Data Silos: An Experimental Study. :fire:'
    affiliation: NUS
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835537
      PDF: https://arxiv.org/abs/2102.02079
      CODE: https://github.com/Xtra-Computing/NIID-Bench
  - title: Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous
      Edge Computing
    affiliation: USTC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835657
  - title: 'Samba: A System for Secure Federated Multi-Armed Bandits'
    affiliation: Univ. Clermont Auvergne
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835585
      CODE: https://github.com/gamarcad/samba-demo
  - title: 'FedRecAttack: Model Poisoning Attack to Federated Recommendation'
    affiliation: ZJU
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835228
      PDF: https://arxiv.org/abs/2204.01499
      CODE: https://github.com/rdz98/fedrecattack
  - title: Enhancing Federated Learning with In-Cloud Unlabeled Data
    affiliation: USTC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835163
  - title: Efficient Participant Contribution Evaluation for Horizontal and Vertical
      Federated Learning
    affiliation: USTC
    venue: ICDE
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9835159
  - title: An Introduction to Federated Computation
    affiliation: University of Warwick; Facebook
    venue: SIGMOD Tutorial
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3514221.3522561
  - title: 'BlindFL: Vertical Federated Machine Learning without Peeking into Your
      Data'
    affiliation: PKU; Tencent
    venue: SIGMOD
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3514221.3526127
      PDF: https://arxiv.org/abs/2206.07975
  - title: An Efficient Approach for Cross-Silo Federated Learning to Rank
    affiliation: BUAA
    venue: ICDE
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9458704
      RELATED PAPER(ZH): https://kns.cnki.net/kcms/detail/detail.aspx?doi=10.13328/j.cnki.jos.006174
  - title: Feature Inference Attack on Model Predictions in Vertical Federated Learning
    affiliation: NUS
    venue: ICDE
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9458672/
      PDF: https://arxiv.org/abs/2010.10152
      CODE: https://github.com/xj231/featureinference-vfl
  - title: Efficient Federated-Learning Model Debugging
    affiliation: USTC
    venue: ICDE
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9458829
  - title: Federated Matrix Factorization with Privacy Guarantee
    affiliation: Purdue
    venue: VLDB
    year: '2021'
    materials:
      PUB: https://www.vldb.org/pvldb/vol15/p900-li.pdf
  - title: Projected Federated Averaging with Heterogeneous Differential Privacy.
    affiliation: Renmin University of China
    venue: VLDB
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.14778/3503585.3503592
      CODE: https://github.com/Emory-AIMS/PFA
  - title: Enabling SQL-based Training Data Debugging for Federated Learning
    affiliation: Simon Fraser University
    venue: VLDB
    year: '2021'
    materials:
      PUB: http://www.vldb.org/pvldb/vol15/p388-wu.pdf
      PDF: https://arxiv.org/abs/2108.11884
      CODE: https://github.com/sfu-db/FedRain-and-Frog
  - title: 'Refiner: A Reliable Incentive-Driven Federated Learning System Powered
      by Blockchain'
    affiliation: ZJU
    venue: VLDB
    year: '2021'
    materials:
      PUB: http://vldb.org/pvldb/vol14/p2659-jiang.pdf
  - title: 'Tanium Reveal: A Federated Search Engine for Querying Unstructured File
      Data on Large Enterprise Networks'
    affiliation: Tanium Inc.
    venue: VLDB
    year: '2021'
    materials:
      PUB: http://www.vldb.org/pvldb/vol14/p3096-stoddard.pdf
      VIDEO: https://www.bilibili.com/video/BV1Wg411j7aA
  - title: 'VF2Boost: Very Fast Vertical Federated Gradient Boosting for Cross-Enterprise
      Learning'
    affiliation: PKU
    venue: SIGMOD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3448016.3457241
  - title: 'ExDRa: Exploratory Data Science on Federated Raw Data'
    affiliation: SIEMENS
    venue: SIGMOD
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3448016.3457549
  - title: Joint blockchain and federated learning-based offloading in harsh edge
      computing environments
    affiliation: TJU
    venue: SIGMOD workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3460866.3461765
  - title: Privacy Preserving Vertical Federated Learning for Tree-based Models
    affiliation: NUS
    venue: VLDB
    year: '2020'
    materials:
      PUB: http://vldb.org/pvldb/vol13/p2090-wu.pdf
      PDF: https://arxiv.org/abs/2008.06170
      VIDEO: https://www.youtube.com/watch?v=sjii8oVCqiY
      CODE: https://github.com/nusdbsystem/pivot
fl-in-top-network-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                                                     '
    venue: '    Venue         '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 60
    venue: 10
    year: 4
    materials: 60
  body:
  - title: 'FLASH: Towards a High-performance Hardware Acceleration Architecture for
      Cross-silo Federated Learning'
    affiliation: HKUST; Clustar
    venue: NSDI
    year: '2023'
    materials:
      PUB: https://www.usenix.org/conference/nsdi23/presentation/zhang-junxue
      SLIDE: https://www.usenix.org/system/files/nsdi23_slides_zhang.pdf
      VIDEO: https://www.youtube.com/watch?v=I5V3r-8sY-Y
  - title: To Store or Not? Online Data Selection for Federated Learning with Limited
      Storage.
    affiliation: SJTU
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583426
      PDF: https://arxiv.org/abs/2209.00195
  - title: 'pFedPrompt: Learning Personalized Prompt for Vision-Language Models in
      Federated Learning.'
    affiliation: PolyU
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583518
  - title: Quantifying and Defending against Privacy Threats on Federated Knowledge
      Graph Embedding.
    affiliation: ZJU; HIC-ZJU
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583450
      PDF: https://arxiv.org/abs/2304.02932
  - title: Vertical Federated Knowledge Transfer via Representation Distillation for
      Healthcare Collaboration Networks
    affiliation: PKU
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583874
      PDF: https://arxiv.org/abs/2302.05675
      CODE: https://github.com/Chung-ju/VFedTrans
  - title: Semi-decentralized Federated Ego Graph Learning for Recommendation
    affiliation: SUST
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583337
      PDF: https://arxiv.org/abs/2302.10900
  - title: 'FlexiFed: Personalized Federated Learning for Edge Clients with Heterogeneous
      Model Architectures.'
    affiliation: Swinburne
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583347
      CODE: https://github.com/fio1982/FlexiFed
  - title: 'FedEdge: Accelerating Edge-Assisted Federated Learning.'
    affiliation: Swinburne
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583264
  - title: Federated Node Classification over Graphs with Latent Link-type Heterogeneity.
    affiliation: Emory University
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583471
      CODE: https://github.com/Oxfordblue7/FedLIT
  - title: 'FedACK: Federated Adversarial Contrastive Knowledge Distillation for Cross-Lingual
      and Cross-Model Social Bot Detection.'
    affiliation: USTC
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583500
      PDF: https://arxiv.org/abs/2303.07113
      CODE: https://github.com/846468230/FedACK
  - title: Interaction-level Membership Inference Attack Against Federated Recommender
      Systems.
    affiliation: UQ
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583359
      PDF: https://arxiv.org/abs/2301.10964
  - title: 'AgrEvader: Poisoning Membership Inference against Byzantine-robust Federated
      Learning.'
    affiliation: Deakin University
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583542
  - title: Heterogeneous Federated Knowledge Graph Embedding Learning and Unlearning.
    affiliation: NJU
    venue: WWW
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543507.3583305
      PDF: https://arxiv.org/abs/2302.02069
      CODE: https://github.com/nju-websoft/FedLU
  - title: 'Federated Learning for Metaverse: A Survey.'
    affiliation: JNU
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587584
      PDF: https://arxiv.org/abs/2303.17987
  - title: Understanding the Impact of Label Skewness and Optimization on Federated
      Learning for Text Classification
    affiliation: KU Leuven
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587599
  - title: 'Privacy-Preserving Online Content Moderation: A Federated Learning Use
      Case.'
    affiliation: CUT
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587604
      PDF: https://arxiv.org/abs/2209.11843
  - title: Privacy-Preserving Online Content Moderation with Federated Learning.
    affiliation: CUT
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587366
  - title: A Federated Learning Benchmark for Drug-Target Interaction.
    affiliation: University of Turin
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587687
      PDF: https://arxiv.org/abs/2302.07684
      CODE: https://github.com/giemp95/feddti
  - title: Towards a Decentralized Data Hub and Query System for Federated Dynamic
      Data Spaces.
    affiliation: TU Berlin
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587646
  - title: 1st Workshop on Federated Learning Technologies1st Workshop on Federated
      Learning Technologies
    affiliation: University of Turin
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3589741
  - title: A Survey of Trustworthy Federated Learning with Perspectives on Security,
      Robustness and Privacy
    affiliation: CUHK
    venue: WWW (Companion Volume)
    year: '2023'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3543873.3587681
      PDF: https://arxiv.org/abs/2302.10637
  - title: A Hierarchical Knowledge Transfer Framework for Heterogeneous Federated
      Learning
    affiliation: THU
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: A Reinforcement Learning Approach for Minimizing Job Completion Time in
      Clustered Federated Learning
    affiliation: Southeast University
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Adaptive Configuration for Heterogeneous Participants in Decentralized
      Federated Learning
    affiliation: USTC
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2212.02136
  - title: 'AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge
      Devices'
    affiliation: Guangdong University of Technology
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2301.03062
  - title: 'AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation'
    affiliation: HUST
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Asynchronous Federated Unlearning
    affiliation: University of Toronto
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://iqua.ece.toronto.edu/papers/ningxinsu-infocom23.pdf
  - title: Communication-Efficient Federated Learning for Heterogeneous Edge Devices
      Based on Adaptive Gradient Quantization
    affiliation: PSU
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2212.08272
  - title: Enabling Communication-Efficient Federated Learning via Distributed Compressed
      Sensing
    affiliation: Beihang University
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Federated Learning under Heterogeneous and Correlated Client Availability
    affiliation: Inria
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2301.04632
      CODE: https://github.com/arodio/ca-fed
  - title: Federated Learning with Flexible Control
    affiliation: IBM
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2212.08496
  - title: Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
    affiliation: The University of Sydney
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2212.12121
  - title: 'FedMoS: Taming Client Drift in Federated Learning with Double Momentum
      and Adaptive Selection'
    affiliation: HUST
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://wangxionghome.github.io/MainFL-TR.pdf
  - title: 'FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated
      Learning'
    affiliation: NTU
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Heterogeneity-Aware Federated Learning with Adaptive Client Selection and
      Gradient Compression
    affiliation: USTC
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Joint Edge Aggregation and Association for Cost-Efficient Multi-Cell Federated
      Learning
    affiliation: NUDT
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Joint Participation Incentive and Network Pricing Design for Federated
      Learning
    affiliation: Northwestern University
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: 'More than Enough is Too Much: Adaptive Defenses against Gradient Leakage
      in Production Federated Learning'
    affiliation: University of Toronto
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://iqua.ece.toronto.edu/papers/feiwang-infocom23.pdf
  - title: 'Network Adaptive Federated Learning: Congestion and Lossy Compression'
    affiliation: UTAustin
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2301.04430
  - title: 'OBLIVION: Poisoning Federated Learning by Inducing Catastrophic Forgetting'
    affiliation: The Hang Seng University of Hong Kong
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: Privacy as a Resource in Differentially Private Federated Learning
    affiliation: BUPT
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: 'SplitGP: Achieving Both Generalization and Personalization in Federated
      Learning'
    affiliation: KAIST
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2212.08343
  - title: 'SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition'
    affiliation: Beihang University
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: 'Tackling System Induced Bias in Federated Learning: Stratification and
      Convergence Analysis'
    affiliation: Southern University of Science and Technology
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2112.11256
  - title: 'Toward Sustainable AI: Federated Learning Demand Response in Cloud-Edge
      Systems via Auctions'
    affiliation: BUPT
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: http://ix.cs.uoregon.edu/~jiao/publications/infocom23-fl.pdf
  - title: Truthful Incentive Mechanism for Federated Learning with Crowdsourced Data
      Labeling
    affiliation: Auburn University
    venue: INFOCOM
    year: '2023'
    materials:
      PDF: https://arxiv.org/abs/2302.00106
  - title: 'TVFL: Tunable Vertical Federated Learning towards Communication-Efficient
      Model Serving'
    affiliation: USTC
    venue: INFOCOM
    year: '2023'
    materials: {}
  - title: 'PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection
      for Efficient Federated Learning'
    affiliation: MSU
    venue: MobiCom
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3495243.3517017
      PDF: https://www.egr.msu.edu/~mizhang/papers/2022_MobiCom_PyramidFL.pdf
      CODE: https://github.com/liecn/PyramidFL
  - title: 'NestFL: efficient federated learning through progressive model pruning
      in heterogeneous edge computing'
    affiliation: pmlabs
    venue: MobiCom(Poster)
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3495243.3558248
  - title: Federated learning-based air quality prediction for smart cities using
      BGRU model
    affiliation: IITM
    venue: MobiCom(Poster)
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3495243.3558267
  - title: 'FedHD: federated learning with hyperdimensional computing'
    affiliation: UCSD
    venue: MobiCom(Demo)
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3495243.3558757
      CODE: https://github.com/QuanlingZhao/FedHD
  - title: Joint Superposition Coding and Training for Federated Learning over Multi-Width
      Neural Networks
    affiliation: Korea University
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796733
  - title: Towards Optimal Multi-Modal Federated Learning on Non-IID Data with Hierarchical
      Gradient Blending
    affiliation: University of Toronto
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796724
  - title: Optimal Rate Adaption in Federated Learning with Compressed Communications
    affiliation: SZU
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796982
      PDF: https://arxiv.org/abs/2112.06694
  - title: 'The Right to be Forgotten in Federated Learning: An Efficient Realization
      with Rapid Retraining.'
    affiliation: CityU
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796721
      PDF: https://arxiv.org/abs/2203.07320
  - title: Tackling System and Statistical Heterogeneity for Federated Learning with
      Adaptive Client Sampling.
    affiliation: CUHK; AIRS ;Yale University
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796935
      PDF: https://arxiv.org/abs/2112.11256
  - title: Communication-Efficient Device Scheduling for Federated Learning Using
      Stochastic Optimization
    affiliation: Army Research Laboratory, Adelphi
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796818
      PDF: https://arxiv.org/abs/2201.07912
  - title: 'FLASH: Federated Learning for Automated Selection of High-band mmWave
      Sectors'
    affiliation: NEU
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796865
      CODE: https://github.com/Batool-Salehi/FL-based-Sector-Selection
  - title: A Profit-Maximizing Model Marketplace with Differentially Private Federated
      Learning
    affiliation: CUHK; AIRS
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796833
  - title: Protect Privacy from Gradient Leakage Attack in Federated Learning
    affiliation: PolyU
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796841/
      SLIDE: https://jxiao.wang/slides/INFOCOM22.pdf
  - title: 'FedFPM: A Unified Federated Analytics Framework for Collaborative Frequent
      Pattern Mining.'
    affiliation: SJTU
    venue: INFOCOM
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9796719
      CODE: https://github.com/HuskyW/FFPA
  - title: An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks
      in Federated Learning
    affiliation: SWJTU;THU
    venue: WWW
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3485447.3512233
      PDF: https://arxiv.org/abs/2002.09843
      CODE: https://github.com/Kira0096/PBPFL
  - title: 'LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence,
      and Latency in Split Learning'
    affiliation: Yonsei University
    venue: WWW
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3485447.3512153
  - title: Federated Unlearning via Class-Discriminative Pruning
    affiliation: PolyU
    venue: WWW
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3485447.3512222
      PDF: https://arxiv.org/abs/2110.11794
      CODE: https://github.com/MoonkeyBoy/Federated-Unlearning-via-Class-Discriminative-Pruning
  - title: 'FedKC: Federated Knowledge Composition for Multilingual Natural Language
      Understanding'
    affiliation: Purdue
    venue: WWW
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3485447.3511988
  - title: Powering Multi-Task Federated Learning with Competitive GPU Resource Sharing.
    affiliation: ''
    venue: WWW (Companion Volume)
    year: '2022'
    materials: {}
  - title: 'Federated Bandit: A Gossiping Approach'
    affiliation: University of California
    venue: SIGMETRICS
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447380
      PDF: https://arxiv.org/abs/2010.12763
  - title: 'Hermes: an efficient federated learning framework for heterogeneous mobile
      clients'
    affiliation: Duke University
    venue: MobiCom
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447993.3483278
  - title: Federated mobile sensing for activity recognition
    affiliation: Samsung AI Center
    venue: MobiCom
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3447993.3488031
      PAGE: https://federatedsensing.gitlab.io/
      TALKS: https://federatedsensing.gitlab.io/talks/
      VIDEO: https://federatedsensing.gitlab.io/program/
  - title: 'Learning for Learning: Predictive Online Control of Federated Learning
      with Edge Provisioning.'
    affiliation: Nanjing University
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488733/
  - title: 'Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms,
      and Implementation.'
    affiliation: Purdue
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488906
      PDF: https://arxiv.org/abs/2101.00787
  - title: 'FAIR: Quality-Aware Federated Learning with Precise User Incentive and
      Model Aggregation'
    affiliation: THU
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488743
  - title: Sample-level Data Selection for Federated Learning
    affiliation: USTC
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488723
  - title: 'To Talk or to Work: Flexible Communication Compression for Energy Efficient
      Federated Learning over Heterogeneous Mobile Edge Devices'
    affiliation: Xidian University; CAS
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488839
      PDF: https://arxiv.org/abs/2012.11804
  - title: Cost-Effective Federated Learning Design
    affiliation: CUHK; AIRS; Yale University
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488679
      PDF: https://arxiv.org/abs/2012.08336
  - title: 'An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods
      Perspective'
    affiliation: The UBC
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488705
  - title: Resource-Efficient Federated Learning with Hierarchical Aggregation in
      Edge Computing
    affiliation: USTC
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488756/
  - title: 'FedServing: A Federated Prediction Serving Framework Based on Incentive
      Mechanism.'
    affiliation: Jinan University; CityU
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488807
      PDF: https://arxiv.org/abs/2012.10566
  - title: 'Federated Learning over Wireless Networks: A Band-limited Coordinated
      Descent Approach'
    affiliation: Arizona State University
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488818
      PDF: https://arxiv.org/abs/2102.07972
  - title: Dual Attention-Based Federated Learning for Wireless Traffic Prediction
    affiliation: King Abdullah University of Science and Technology
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488883
      PDF: https://arxiv.org/abs/2110.05183
      CODE: https://github.com/chuanting/fedda
  - title: 'FedSens: A Federated Learning Approach for Smart Health Sensing with Class
      Imbalance in Resource Constrained Edge Computing'
    affiliation: University of Notre Dame
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488776/
  - title: 'P-FedAvg: Parallelizing Federated Learning with Theoretical Guarantees'
    affiliation: SYSU; Guangdong Key Laboratory of Big Data Analysis and Processing
    venue: INFOCOM
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9488877
  - title: 'Meta-HAR: Federated Representation Learning for Human Activity Recognition.'
    affiliation: University of Alberta
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3450006
      PDF: https://arxiv.org/abs/2106.00615
      CODE: https://github.com/Chain123/Meta-HAR
  - title: 'PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization'
    affiliation: PKU
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3449847
      PDF: https://arxiv.org/abs/2103.01548
      CODE: https://github.com/lebyni/PFA
  - title: Communication Efficient Federated Generalized Tensor Factorization for
      Collaborative Health Data Analytics
    affiliation: Emory
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3449832
      CODE: https://github.com/jma78/FedGTF-EF
  - title: Hierarchical Personalized Federated Learning for User Modeling
    affiliation: USTC
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3449926
  - title: Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale
      Smartphone Data
    affiliation: PKU
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3449851
      PDF: https://arxiv.org/abs/2006.06983
      SLIDE: https://qipengwang.github.io/files/www21.slides.pdf
      CODE: https://github.com/PKU-Chengxu/FLASH
  - title: Incentive Mechanism for Horizontal Federated Learning Based on Reputation
      and Reverse Auction
    affiliation: SYSU
    venue: WWW
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3442381.3449888
  - title: Physical-Layer Arithmetic for Federated Learning in Uplink MU-MIMO Enabled
      Wireless Networks.
    affiliation: Nanjing University
    venue: INFOCOM
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9155479
  - title: 'Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
      :fire:'
    affiliation: University of Toronto
    venue: INFOCOM
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9155494
      SLIDE: https://workshoputrgv.github.io/slides/hao_wang.pdf
      CODE: https://github.com/iQua/flsim
      解读: https://zhuanlan.zhihu.com/p/458716656
  - title: Enabling Execution Assurance of Federated Learning at Untrusted Participants
    affiliation: THU
    venue: INFOCOM
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9155414
      CODE: https://github.com/zeyu-zh/TrustFL
  - title: 'Billion-scale federated learning on mobile clients: a submodel design
      with tunable privacy'
    affiliation: SJTU
    venue: MobiCom
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3372224.3419188
  - title: 'Federated Learning over Wireless Networks: Optimization Model Design and
      Analysis'
    affiliation: The University of Sydney
    venue: INFOCOM
    year: '2019'
    materials:
      PUB: https://ieeexplore.ieee.org/document/8737464
      CODE: https://github.com/nhatminh/FEDL-INFOCOM
  - title: 'Beyond Inferring Class Representatives: User-Level Privacy Leakage From
      Federated Learning'
    affiliation: Wuhan University
    venue: INFOCOM
    year: '2019'
    materials:
      PUB: https://ieeexplore.ieee.org/document/8737416
      PDF: https://arxiv.org/abs/1812.00535
      UC.: https://github.com/JonasGeiping/breaching
  - title: 'InPrivate Digging: Enabling Tree-based Distributed Data Mining with Differential
      Privacy'
    affiliation: Collaborative Innovation Center of Geospatial Technology
    venue: INFOCOM
    year: '2018'
    materials:
      PUB: https://ieeexplore.ieee.org/document/8486352
fl-in-top-system-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                  '
    venue: '    Venue                        '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 25
    venue: 25
    year: 4
    materials: 60
  body:
  - title: Optimizing Training Efficiency and Cost of Hierarchical Federated Learning
      in Heterogeneous Mobile-Edge Cloud Computing
    affiliation: ECNU
    venue: TCAD
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9882092
  - title: Type-Aware Federated Scheduling for Typed DAG Tasks on Heterogeneous Multicore
      Platforms
    affiliation: TU Dortmund University
    venue: TC
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9869701
      CODE: https://github.com/Strange369/TypedDAG_on_HeteroMP
  - title: 'Sandbox Computing: A Data Privacy Trusted Sharing Paradigm Via Blockchain
      and Federated Learning.'
    affiliation: BUPT
    venue: TC
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9791849/
  - title: Incentive Mechanism Design for Joint Resource Allocation in Blockchain-Based
      Federated Learning.
    affiliation: IUPUI
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10061576
      PDF: https://arxiv.org/abs/2202.10938
  - title: 'HiFlash: Communication-Efficient Hierarchical Federated Learning With
      Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association.'
    affiliation: ''
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10021868
      PDF: https://arxiv.org/abs/2301.06447
  - title: 'From Deterioration to Acceleration: A Calibration Approach to Rehabilitating
      Step Asynchronism in Federated Optimization.'
    affiliation: ''
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10070571
      PDF: https://arxiv.org/abs/2112.09355
      CODE: https://github.com/harliwu/fedagrac
  - title: Federated Learning Over Coupled Graphs
    affiliation: XJTU
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10029878
      PDF: https://arxiv.org/abs/2301.11099
  - title: 'Privacy vs. Efficiency: Achieving Both Through Adaptive Hierarchical Federated
      Learning'
    affiliation: NUDT
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10043027
  - title: On Model Transmission Strategies in Federated Learning With Lossy Communications
    affiliation: SZU
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10032555
  - title: Scheduling Algorithms for Federated Learning With Minimal Energy Consumption
    affiliation: University of Bordeaux
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10032558
      PDF: https://arxiv.org/abs/2209.06210
      CODE: https://github.com/llpilla/energy-optimal-federated-learning
  - title: Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems
    affiliation: HIT
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10032710
      PDF: https://arxiv.org/abs/2103.07150
  - title: Personalized Edge Intelligence via Federated Self-Knowledge Distillation.
    affiliation: HUST
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9964434/
      CODE: https://github.com/CGCL-codes/pFedSD
  - title: Design of a Quantization-Based DNN Delta Compression Framework for Model
      Snapshots and Federated Learning.
    affiliation: HIT
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10018182
  - title: Multi-Job Intelligent Scheduling With Cross-Device Federated Learning.
    affiliation: Baidu
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9964248
      PDF: https://arxiv.org/abs/2211.13430
  - title: Data-Centric Client Selection for Federated Learning Over Distributed Edge
      Networks.
    affiliation: IIT
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9930629
  - title: 'GossipFL: A Decentralized Federated Learning Framework With Sparsified
      and Adaptive Communication.'
    affiliation: HKBU
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9996127
  - title: 'FedMDS: An Efficient Model Discrepancy-Aware Semi-Asynchronous Clustered
      Federated Learning Framework.'
    affiliation: CQU
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/10018536
  - title: 'HierFedML: Aggregator Placement and UE Assignment for Hierarchical Federated
      Learning in Mobile Edge Computing.'
    affiliation: DUT
    venue: TPDS
    year: '2023'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9935309
  - title: 'BAFL: A Blockchain-Based Asynchronous Federated Learning Framework'
    affiliation: ''
    venue: TC
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9399813/
      CODE: https://github.com/xuchenhao001/AFL
  - title: 'L4L: Experience-Driven Computational Resource Control in Federated Learning'
    affiliation: ''
    venue: TC
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9384231
  - title: Adaptive Federated Learning on Non-IID Data With Resource Constraint
    affiliation: ''
    venue: TC
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9496155
  - title: Locking Protocols for Parallel Real-Time Tasks With Semaphores Under Federated
      Scheduling.
    affiliation: ''
    venue: TCAD
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9542981
  - title: Client Scheduling and Resource Management for Efficient Training in Heterogeneous
      IoT-Edge Federated Learning
    affiliation: ECNU
    venue: TCAD
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9530450
  - title: 'PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems.'
    affiliation: ECNU
    venue: TCAD
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9925684
  - title: 'FHDnn: communication efficient and robust federated learning for AIoT
      networks'
    affiliation: UC San Diego
    venue: DAC
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3489517.3530394
  - title: A Decentralized Federated Learning Framework via Committee Mechanism With
      Convergence Guarantee
    affiliation: SYSU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9870745
      PDF: https://arxiv.org/abs/2108.00365
  - title: Improving Federated Learning With Quality-Aware User Incentive and Auto-Weighted
      Model Aggregation
    affiliation: THU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9847055
  - title: '$f$funcX: Federated Function as a Service for Science.'
    affiliation: SUST
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9899739
      PDF: https://arxiv.org/abs/2209.11631
  - title: 'Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance
      Analysis and Resource Allocation'
    affiliation: NUST
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9664296
      PDF: https://arxiv.org/abs/2101.06905
      CODE: https://github.com/ElvisShaoYumeng/BLADE-FL
  - title: Adaptive Federated Deep Reinforcement Learning for Proactive Content Caching
      in Edge Computing.
    affiliation: CQU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9868114
  - title: 'TDFL: Truth Discovery Based Byzantine Robust Federated Learning'
    affiliation: BIT
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9887909
  - title: Federated Learning With Nesterov Accelerated Gradient
    affiliation: The University of Sydney
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9891808
      PDF: https://arxiv.org/abs/2009.08716
  - title: 'FedGraph: Federated Graph Learning with Intelligent Sampling'
    affiliation: UoA
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/abstract/document/9606516/
      CODE: https://github.com/cfh19980612/FedGraph
      解读: https://zhuanlan.zhihu.com/p/442233479
  - title: 'AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient
      Federated Learning.'
    affiliation: THU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9647925
  - title: 'DONE: Distributed Approximate Newton-type Method for Federated Edge Learning.'
    affiliation: University of Sydney
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9695269
      PDF: https://arxiv.org/abs/2012.05625
      CODE: https://github.com/dual-grp/DONE
  - title: Flexible Clustered Federated Learning for Client-Level Data Distribution
      Shift.
    affiliation: CQU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9647969
      PDF: https://arxiv.org/abs/2108.09749
      CODE: https://github.com/morningd/flexcfl
  - title: Min-Max Cost Optimization for Efficient Hierarchical Federated Learning
      in Wireless Edge Networks.
    affiliation: Xidian University
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9629331
  - title: 'LightFed: An Efficient and Secure Federated Edge Learning System on Model
      Splitting.'
    affiliation: CSU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9613755
  - title: On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized
      Federated Learning.
    affiliation: Purdue
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9664349
      PDF: https://arxiv.org/abs/2011.10643
      CODE: https://github.com/anishacharya/DeLiCoCo
  - title: Incentive-Aware Autonomous Client Participation in Federated Learning.
    affiliation: Sun Yat-sen University
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9705080
  - title: Communicational and Computational Efficient Federated Domain Adaptation.
    affiliation: HKUST
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9757821
  - title: 'Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework
      for Hierarchical Federated Learning.'
    affiliation: NTU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9479786
  - title: Differentially Private Byzantine-Robust Federated Learning.
    affiliation: Qufu Normal University
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9757841
  - title: Multi-Task Federated Learning for Personalised Deep Neural Networks in
      Edge Computing.
    affiliation: University of Exeter
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9492755
      PDF: https://arxiv.org/abs/2007.09236
      CODE: https://github.com/JedMills/MTFL-For-Personalised-DNNs
  - title: Reputation-Aware Hedonic Coalition Formation for Efficient Serverless Hierarchical
      Federated Learning.
    affiliation: BUAA
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9665214
  - title: Differentially Private Federated Temporal Difference Learning.
    affiliation: Stony Brook University
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9645233
  - title: Towards Efficient and Stable K-Asynchronous Federated Learning With Unbounded
      Stale Gradients on Non-IID Data.
    affiliation: XJTU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9712243
      PDF: https://arxiv.org/abs/2203.01214
  - title: Communication-Efficient Federated Learning With Compensated Overlap-FedAvg.
    affiliation: SCU
    venue: TPDS
    year: '2022'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9459540
      PDF: https://arxiv.org/abs/2012.06706
      CODE: https://github.com/Soptq/Overlap-FedAvg
  - title: 'PAPAYA: Practical, Private, and Scalable Federated Learning.'
    affiliation: Meta AI
    venue: MLSys
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2111.04877
      PUB: https://proceedings.mlsys.org/paper/2022/hash/f340f1b1f65b6df5b5e3f94d95b11daf-Abstract.html
  - title: 'LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation
      in Federated Learning'
    affiliation: USC
    venue: MLSys
    year: '2022'
    materials:
      PDF: https://arxiv.org/abs/2109.14236
      PUB: https://proceedings.mlsys.org/paper/2022/hash/d2ddea18f00665ce8623e36bd4e3c7c5-Abstract.html
      CODE: https://github.com/LightSecAgg/MLSys2022_anonymous
  - title: 'SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low
      Overhead'
    affiliation: University of Warwick
    venue: TC
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/1910.01355
      PUB: https://ieeexplore.ieee.org/document/9093123
      CODE: https://github.com/wingter562/SAFA
  - title: Efficient Federated Learning for Cloud-Based AIoT Applications
    affiliation: ECNU
    venue: TCAD
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9302596
  - title: 'HADFL: Heterogeneity-aware Decentralized Federated Learning Framework'
    affiliation: USTC
    venue: DAC
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/2111.08274
      PUB: https://ieeexplore.ieee.org/document/9586101
  - title: 'Helios: Heterogeneity-Aware Federated Learning with Dynamically Balanced
      Collaboration.'
    affiliation: GMU
    venue: DAC
    year: '2021'
    materials:
      PDF: https://arxiv.org/abs/1912.01684
      PUB: https://ieeexplore.ieee.org/document/9586241
  - title: 'FedLight: Federated Reinforcement Learning for Autonomous Multi-Intersection
      Traffic Signal Control.'
    affiliation: ECNU
    venue: DAC
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9586175
  - title: 'Oort: Efficient Federated Learning via Guided Participant Selection'
    affiliation: University of Michigan
    venue: OSDI
    year: '2021'
    materials:
      PUB: https://www.usenix.org/conference/osdi21/presentation/lai
      PDF: https://arxiv.org/abs/2010.06081
      CODE: https://github.com/SymbioticLab/Oort
      SLIDES: https://www.usenix.org/system/files/osdi21_slides_lai.pdf
      VIDEO: https://www.youtube.com/watch?v=5npOel4T4Mw
  - title: Towards Efficient Scheduling of Federated Mobile Devices Under Computational
      and Statistical Heterogeneity.
    affiliation: Old Dominion University
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9195793
      PDF: https://arxiv.org/abs/2005.12326
  - title: Self-Balancing Federated Learning With Global Imbalanced Data in Mobile
      Systems.
    affiliation: CQU
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9141436
      CODE: https://github.com/mtang724/Self-Balancing-Federated-Learning
  - title: An Efficiency-Boosting Client Selection Scheme for Federated Learning With
      Fairness Guarantee
    affiliation: SCUT
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9272649/
      PDF: https://arxiv.org/abs/2011.01783
      解读: https://zhuanlan.zhihu.com/p/456101770
  - title: 'Proof of Federated Learning: A Novel Energy-Recycling Consensus Algorithm.'
    affiliation: Beijing Normal University
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9347812
      PDF: https://arxiv.org/abs/1912.11745
  - title: 'Biscotti: A Blockchain System for Private and Secure Federated Learning.'
    affiliation: UBC
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9292450
  - title: Mutual Information Driven Federated Learning.
    affiliation: Deakin University
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9272656
  - title: Accelerating Federated Learning Over Reliability-Agnostic Clients in Mobile
      Edge Computing Systems.
    affiliation: University of Warwick
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9272671
      PDF: https://arxiv.org/abs/2007.14374
  - title: 'FedSCR: Structure-Based Communication Reduction for Federated Learning.'
    affiliation: HKU
    venue: TPDS
    year: '2021'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9303442
  - title: 'FedScale: Benchmarking Model and System Performance of Federated Learning
      :fire:'
    affiliation: University of Michigan
    venue: SOSP workshop / ICML 2022
    year: '2021'
    materials:
      PUB: https://proceedings.mlr.press/v162/lai22a.html
      PDF: https://arxiv.org/abs/2105.11367
      CODE: https://github.com/SymbioticLab/FedScale
      解读: https://zhuanlan.zhihu.com/p/520020117
  - title: Redundancy in cost functions for Byzantine fault-tolerant federated learning
    affiliation: ''
    venue: SOSP workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477114.3488761
  - title: Towards an Efficient System for Differentially-private, Cross-device Federated
      Learning
    affiliation: ''
    venue: SOSP workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477114.3488762
  - title: 'GradSec: a TEE-based Scheme Against Federated Learning Inference Attacks'
    affiliation: ''
    venue: SOSP workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477114.3488763
  - title: Community-Structured Decentralized Learning for Resilient EI.
    affiliation: ''
    venue: SOSP workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477114.3488764
  - title: Separation of Powers in Federated Learning (Poster Paper)
    affiliation: IBM Research
    venue: SOSP workshop
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477114.3488765
      PDF: https://arxiv.org/abs/2105.09400
  - title: Accelerating Federated Learning via Momentum Gradient Descent.
    affiliation: USTC
    venue: TPDS
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9003425
      PDF: https://arxiv.org/abs/1910.03197
  - title: Towards Fair and Privacy-Preserving Federated Deep Models.
    affiliation: NUS
    venue: TPDS
    year: '2020'
    materials:
      PUB: https://ieeexplore.ieee.org/document/9098045
      PDF: https://arxiv.org/abs/1906.01167
      CODE: https://github.com/lingjuanlv/FPPDL
  - title: 'Federated Optimization in Heterogeneous Networks :fire:'
    affiliation: CMU
    venue: MLSys
    year: '2020'
    materials:
      PUB: https://proceedings.mlsys.org/paper/2020/hash/38af86134b65d0f10fe33d30dd76442e-Abstract.html
      PDF: https://arxiv.org/abs/1812.06127
      CODE: https://github.com/litian96/FedProx
  - title: 'Towards Federated Learning at Scale: System Design'
    affiliation: Google
    venue: MLSys
    year: '2019'
    materials:
      PUB: https://proceedings.mlsys.org/paper/2019/hash/bd686fd640be98efaae0091fa301e613-Abstract.html
      PDF: https://arxiv.org/abs/1902.01046
      解读: https://zhuanlan.zhihu.com/p/450993635
federated-learning-framework:
  header:
    platform: 'Platform                                                        '
    papers: '    Papers                                                          '
    affiliations: '    Affiliations                                                    '
    sg: '                     SG                     '
    st: '                     ST                     '
    materials: '    Materials'
  length:
    platform: 60
    papers: 60
    affiliations: 60
    sg: 36
    st: 36
    materials: 60
  body:
  - platform: '[PySyft](https://github.com/OpenMined/PySyft)<br />[![Stars](https://img.shields.io/github/stars/OpenMined/PySyft.svg?color=red)](https://github.com/OpenMined/PySyft/stargazers)<br
      />![](https://img.shields.io/github/last-commit/OpenMined/PySyft)'
    papers: '[A generic framework for privacy preserving deep learning](https://arxiv.org/abs/1811.04017)'
    affiliations: '[OpenMined](https://www.openmined.org/)'
    sg: ''
    st: ''
    materials:
      DOC: https://pysyft.readthedocs.io/en/latest/installing.html
  - platform: '[FATE](https://github.com/FederatedAI/FATE)<br />[![Stars](https://img.shields.io/github/stars/FederatedAI/FATE.svg?color=red)](https://github.com/FederatedAI/FATE/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FederatedAI/FATE)'
    papers: '[FATE: An Industrial Grade Platform for Collaborative Learning With Data
      Protection](https://www.jmlr.org/papers/volume22/20-815/20-815.pdf)'
    affiliations: '[WeBank](https://fedai.org/)'
    sg: ''
    st: ':white_check_mark::white_check_mark:'
    materials:
      DOC: https://fate.readthedocs.io/en/latest/
      DOC(ZH): https://fate.readthedocs.io/en/latest/zh/
  - platform: '[MindSpore Federated](https://github.com/mindspore-ai/mindspore/tree/master/tests/st/fl)<br
      />[![Stars](https://img.shields.io/github/stars/mindspore-ai/mindspore.svg?color=red)](https://github.com/mindspore-ai/mindspore/stargazers)<br
      />![](https://img.shields.io/github/last-commit/mindspore-ai/mindspore)'
    papers: ''
    affiliations: HUAWEI
    sg: ''
    st: ''
    materials:
      DOC: https://mindspore.cn/federated/docs/zh-CN/r1.6/index.html
      PAGE: https://mindspore.cn/federated
  - platform: '[FedML](https://github.com/FedML-AI/FedML)<br />[![Stars](https://img.shields.io/github/stars/FedML-AI/FedML.svg?color=red)](https://github.com/FedML-AI/FedML/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FedML-AI/FedML)'
    papers: '[FedML: A Research Library and Benchmark for Federated Machine Learning](https://arxiv.org/abs/2007.13518)'
    affiliations: '[FedML](https://fedml.ai/)'
    sg: ':white_check_mark::white_check_mark:'
    st: ':white_check_mark:'
    materials:
      DOC: https://doc.fedml.ai/
  - platform: '[TFF(Tensorflow-Federated)](https://github.com/tensorflow/federated)
      <br />[![Stars](https://img.shields.io/github/stars/tensorflow/federated.svg?color=red)](https://github.com/tensorflow/federated/stargazers)<br
      />![](https://img.shields.io/github/last-commit/tensorflow/federated)'
    papers: '[Towards Federated Learning at Scale: System Design](https://arxiv.org/abs/1902.01046)'
    affiliations: Google
    sg: ''
    st: ''
    materials:
      DOC: https://www.tensorflow.org/federated
      PAGE: https://www.tensorflow.org/federated
  - platform: '[Flower](https://github.com/adap/flower)<br />[![Stars](https://img.shields.io/github/stars/adap/flower.svg?color=red)](https://github.com/adap/flower/stargazers)<br
      />![](https://img.shields.io/github/last-commit/adap/flower)'
    papers: '[Flower: A Friendly Federated Learning Research Framework](https://arxiv.org/abs/2104.03042.pdf)'
    affiliations: '[flower.dev](https://flower.dev/) [adap](https://adap.com/en)'
    sg: ''
    st: ''
    materials:
      DOC: https://flower.dev/docs/
  - platform: '[SecretFlow](https://github.com/secretflow/secretflow) <br />[![Stars](https://img.shields.io/github/stars/secretflow/secretflow.svg?color=red)](https://github.com/secretflow/secretflow/stargazers)<br
      />![](https://img.shields.io/github/last-commit/secretflow/secretflow)'
    papers: ''
    affiliations: '[Ant group](https://www.antgroup.com/)'
    sg: ''
    st: ':white_check_mark:'
    materials:
      DOC: https://secretflow.readthedocs.io/en/latest/getting_started/index.html
  - platform: '[FederatedScope](https://github.com/alibaba/FederatedScope)<br />[![Stars](https://img.shields.io/github/stars/alibaba/FederatedScope.svg?color=blue)](https://github.com/alibaba/FederatedScope/stargazers)<br
      />![](https://img.shields.io/github/last-commit/alibaba/FederatedScope)'
    papers: '[FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://arxiv.org/abs/2204.05011)'
    affiliations: '[Alibaba DAMO Academy](https://damo.alibaba.com/labs/data-analytics-and-intelligence)'
    sg: ':white_check_mark::white_check_mark:'
    st: ''
    materials:
      DOC: https://federatedscope.io/refs/index
      PAGE: https://federatedscope.io/
  - platform: '[Fedlearner](https://github.com/bytedance/fedlearner)<br />[![Stars](https://img.shields.io/github/stars/bytedance/fedlearner.svg?color=blue)](https://github.com/bytedance/fedlearner/stargazers)<br
      />![](https://img.shields.io/github/last-commit/bytedance/fedlearner)'
    papers: ''
    affiliations: '[Bytedance](https://github.com/bytedance)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[LEAF](https://github.com/TalwalkarLab/leaf)<br />[![Stars](https://img.shields.io/github/stars/TalwalkarLab/leaf.svg?color=blue)](https://github.com/TalwalkarLab/leaf/stargazers)<br
      />![](https://img.shields.io/github/last-commit/TalwalkarLab/leaf)'
    papers: '[LEAF: A Benchmark for Federated Settings](https://arxiv.org/abs/1812.01097.pdf)'
    affiliations: '[CMU](https://leaf.cmu.edu/)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[Rosetta](https://github.com/LatticeX-Foundation/Rosetta)<br />[![Stars](https://img.shields.io/github/stars/LatticeX-Foundation/Rosetta.svg?color=blue)](https://github.com/LatticeX-Foundation/Rosetta/stargazers)<br
      />![](https://img.shields.io/github/last-commit/LatticeX-Foundation/Rosetta)'
    papers: ''
    affiliations: '[matrixelements](https://www.matrixelements.com/product/rosetta)'
    sg: ''
    st: ''
    materials:
      DOC: https://github.com/LatticeX-Foundation/Rosetta/blob/master/doc/DEPLOYMENT.md
      PAGE: https://github.com/LatticeX-Foundation/Rosetta
  - platform: '[OpenFL](https://github.com/intel/openfl)<br />[![Stars](https://img.shields.io/github/stars/intel/openfl.svg?color=blue)](https://github.com/intel/openfl/stargazers)<br
      />![](https://img.shields.io/github/last-commit/intel/openfl)'
    papers: '[OpenFL: An open-source framework for Federated Learning](https://arxiv.org/abs/2105.06413)'
    affiliations: '[Intel](https://github.com/intel)'
    sg: ''
    st: ''
    materials:
      DOC: https://openfl.readthedocs.io/en/latest/install.html
  - platform: '[PFL-Non-IID](https://github.com/TsingZ0/PFL-Non-IID)<br />[![Stars](https://img.shields.io/github/stars/TsingZ0/PFL-Non-IID.svg?color=blue)](https://github.com/TsingZ0/PFL-Non-IID/stargazers)<br
      />![](https://img.shields.io/github/last-commit/TsingZ0/PFL-Non-IID)'
    papers: ''
    affiliations: SJTU
    sg: ''
    st: ''
    materials: {}
  - platform: '[Fedlab](https://github.com/SMILELab-FL/FedLab)<br />[![Stars](https://img.shields.io/github/stars/SMILELab-FL/FedLab.svg?color=blue)](https://github.com/SMILELab-FL/FedLab/stargazers)<br
      />![](https://img.shields.io/github/last-commit/SMILELab-FL/FedLab)'
    papers: '[FedLab: A Flexible Federated Learning Framework](https://arxiv.org/abs/2107.11621)'
    affiliations: '[SMILELab](https://github.com/SMILELab-FL/)'
    sg: ''
    st: ''
    materials:
      DOC: https://fedlab.readthedocs.io/en/master/
      DOC(ZH): https://fedlab.readthedocs.io/zh_CN/latest/
      PAGE: https://github.com/SMILELab-FL/FedLab-benchmarks
  - platform: '[PaddleFL](https://github.com/PaddlePaddle/PaddleFL)<br />[![Stars](https://img.shields.io/github/stars/PaddlePaddle/PaddleFL.svg?color=blue)](https://github.com/PaddlePaddle/PaddleFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/PaddlePaddle/PaddleFL)'
    papers: ''
    affiliations: Baidu
    sg: ''
    st: ''
    materials:
      DOC: https://paddlefl.readthedocs.io/en/latest/index.html
  - platform: '[IBM Federated Learning](https://github.com/IBM/federated-learning-lib)<br
      />[![Stars](https://img.shields.io/github/stars/IBM/federated-learning-lib.svg?color=blue)](https://github.com/IBM/federated-learning-lib/stargazers)<br
      />![](https://img.shields.io/github/last-commit/IBM/federated-learning-lib)'
    papers: '[IBM Federated Learning: an Enterprise Framework White Paper](https://arxiv.org/abs/2007.10987.pdf)'
    affiliations: '[IBM](https://github.com/IBM)'
    sg: ''
    st: ':white_check_mark:'
    materials:
      PAPERS: https://github.com/IBM/federated-learning-lib/blob/main/docs/papers.md
  - platform: '[Privacy Meter](https://github.com/privacytrustlab/ml_privacy_meter)<br
      />[![Stars](https://img.shields.io/github/stars/privacytrustlab/ml_privacy_meter.svg?color=blue)](https://github.com/PaddlePaddle/privacytrustlab/ml_privacy_meter)<br
      />![](https://img.shields.io/github/last-commit/privacytrustlab/ml_privacy_meter)'
    papers: '[Comprehensive Privacy Analysis of Deep Learning: Passive and Active
      White-box Inference Attacks against Centralized and Federated Learning](https://ieeexplore.ieee.org/document/8835245)'
    affiliations: University of Massachusetts Amherst
    sg: ''
    st: ''
    materials: {}
  - platform: '[Primihub](https://github.com/primihub/primihub)<br />[![Stars](https://img.shields.io/github/stars/primihub/primihub.svg?color=blue)](https://github.com/primihub/primihub/stargazers)<br
      />![](https://img.shields.io/github/last-commit/primihub/primihub)'
    papers: ''
    affiliations: '[primihub](https://github.com/primihub)'
    sg: ''
    st: ''
    materials:
      DOC: ''
  - platform: '[KubeFATE](https://github.com/FederatedAI/KubeFATE)<br />[![Stars](https://img.shields.io/github/stars/FederatedAI/KubeFATE.svg?color=blue)](https://github.com/FederatedAI/KubeFATE/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FederatedAI/KubeFATE)'
    papers: ''
    affiliations: '[WeBank](https://fedai.org/)'
    sg: ''
    st: ''
    materials:
      WIKI: https://github.com/FederatedAI/KubeFATE/wiki/#faqs
  - platform: '[NVFlare](https://github.com/NVIDIA/NVFlare)<br />[![Stars](https://img.shields.io/github/stars/NVIDIA/NVFlare.svg?color=blue)](https://github.com/NVIDIA/NVFlare/stargazers)<br
      />![](https://img.shields.io/github/last-commit/NVIDIA/NVFlare)'
    papers: ''
    affiliations: '[NVIDIA](https://github.com/NVIDIA)'
    sg: ''
    st: ''
    materials:
      DOC: https://nvflare.readthedocs.io/en/2.1.1/
  - platform: '[NIID-Bench](https://github.com/Xtra-Computing/NIID-Bench)<br />[![Stars](https://img.shields.io/github/stars/Xtra-Computing/NIID-Bench.svg?color=blue)](https://github.com/Xtra-Computing/NIID-Bench/stargazers)<br
      />![](https://img.shields.io/github/last-commit/Xtra-Computing/NIID-Bench)'
    papers: '[Federated Learning on Non-IID Data Silos: An Experimental Study](https://arxiv.org/abs/2102.02079.pdf)'
    affiliations: '[Xtra Computing Group](https://github.com/Xtra-Computing)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[Differentially Private Federated Learning: A Client-level Perspective](https://github.com/SAP-samples/machine-learning-diff-private-federated-learning)
      <br />[![Stars](https://img.shields.io/github/stars/SAP-samples/machine-learning-diff-private-federated-learning.svg?color=blue)](https://github.comSAP-samples/machine-learning-diff-private-federated-learning/stargazers)<br
      />![](https://img.shields.io/github/last-commit/SAP-samples/machine-learning-diff-private-federated-learning)'
    papers: '[Differentially Private Federated Learning: A Client Level Perspective](https://arxiv.org/abs/1712.07557)'
    affiliations: '[SAP-samples](https://github.com/SAP-samples)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[FedScale](https://github.com/SymbioticLab/FedScale)<br />[![Stars](https://img.shields.io/github/stars/SymbioticLab/FedScale.svg?color=blue)](https://github.com/SymbioticLab/FedScale/stargazers)<br
      />![](https://img.shields.io/github/last-commit/SymbioticLab/FedScale)'
    papers: '[FedScale: Benchmarking Model and System Performance of Federated Learning
      at Scale](https://arxiv.org/abs/2105.11367.pdf)'
    affiliations: '[SymbioticLab(U-M)](https://symbioticlab.org/)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[easyFL](https://github.com/WwZzz/easyFL)<br />[![Stars](https://img.shields.io/github/stars/WwZzz/easyFL.svg?color=blue)](https://github.com/WwZzz/easyFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/WwZzz/easyFL)'
    papers: '[Federated Learning with Fair Averaging](https://www.ijcai.org/proceedings/2021/223)'
    affiliations: XMU
    sg: ''
    st: ''
    materials: {}
  - platform: '[Backdoors 101](https://github.com/ebagdasa/backdoors101)<br />[![Stars](https://img.shields.io/github/stars/ebagdasa/backdoors101.svg?color=blue)](https://github.com/ebagdasa/backdoors101/stargazers)<br
      />![](https://img.shields.io/github/last-commit/ebagdasa/backdoors101)'
    papers: '[Blind Backdoors in Deep Learning Models](https://arxiv.org/abs/2005.03823)'
    affiliations: Cornell Tech
    sg: ''
    st: ''
    materials: {}
  - platform: '[SWARM LEARNING](https://github.com/HewlettPackard/swarm-learning)
      <br />[![Stars](https://img.shields.io/github/stars/HewlettPackard/swarm-learning.svg?color=blue)](https://github.com/HewlettPackard/swarm-learning/stargazers)<br
      />![](https://img.shields.io/github/last-commit/HewlettPackard/swarm-learning)'
    papers: '[Swarm Learning for decentralized and confidential clinical machine learning](https://www.nature.com/articles/s41586-021-03583-3)'
    affiliations: ''
    sg: ''
    st: ''
    materials:
      VIDEO: https://github.com/HewlettPackard/swarm-learning/blob/master/docs/videos.md
  - platform: '[substra](https://github.com/Substra/substra) <br />[![Stars](https://img.shields.io/github/stars/Substra/substra.svg?color=blue)](https://github.com/Substra/substra/stargazers)<br
      />![](https://img.shields.io/github/last-commit/Substra/substra)'
    papers: ''
    affiliations: '[Substra](https://github.com/Substra)'
    sg: ''
    st: ''
    materials:
      DOC: https://doc.substra.ai/index.html
  - platform: '[FedJAX](https://github.com/google/fedjax)<br />[![Stars](https://img.shields.io/github/stars/google/fedjax.svg?color=blue)](https://github.com/google/fedjax/stargazers)<br
      />![](https://img.shields.io/github/last-commit/google/fedjax)'
    papers: '[FEDJAX: Federated learning simulation with JAX](https://arxiv.org/abs/2108.02117.pdf)'
    affiliations: '[Google](https://ai.googleblog.com/2021/10/fedjax-federated-learning-simulation.html)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[plato](https://github.com/TL-System/plato)<br />[![Stars](https://img.shields.io/github/stars/TL-System/plato.svg?color=blue)](https://github.com/TL-System/plato/stargazers)<br
      />![](https://img.shields.io/github/last-commit/TL-System/plato)'
    papers: ''
    affiliations: UofT
    sg: ''
    st: ''
    materials: {}
  - platform: '[FedNLP](https://github.com/FedML-AI/FedNLP)<br />[![Stars](https://img.shields.io/github/stars/FedML-AI/FedNLP.svg?color=blue)](https://github.com/FedML-AI/FedNLP/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FedML-AI/FedNLP)'
    papers: '[FedNLP: Benchmarking Federated Learning Methods for Natural Language
      Processing Tasks](https://arxiv.org/abs/2104.08815)'
    affiliations: '[FedML](https://fedml.ai/)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[Galaxy Federated Learning](https://github.com/GalaxyLearning/GFL)<br
      />[![Stars](https://img.shields.io/github/stars/GalaxyLearning/GFL.svg?color=blue)](https://github.com/GalaxyLearning/GFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/GalaxyLearning/GFL)'
    papers: '[GFL: A Decentralized Federated Learning Framework Based On Blockchain](https://arxiv.org/abs/2010.10996.pdf)'
    affiliations: ZJU
    sg: ''
    st: ''
    materials:
      DOC: http://galaxylearning.github.io/
  - platform: '[Xaynet](https://github.com/xaynetwork/xaynet)<br />[![Stars](https://img.shields.io/github/stars/xaynetwork/xaynet.svg?color=blue)](https://github.com/xaynetwork/xaynet/stargazers)<br
      />![](https://img.shields.io/github/last-commit/xaynetwork/xaynet)'
    papers: ''
    affiliations: '[XayNet](https://www.xayn.com/)'
    sg: ''
    st: ''
    materials:
      PAGE: https://www.xaynet.dev/
      DOC: https://docs.rs/xaynet
      WHITEPAPER: https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5f157004da6585f299fa542b_XayNet%20Whitepaper%202.1.pdf
      LEGAL REVIEW: https://uploads-ssl.webflow.com/5f0c5c0bb18a279f0a62919e/5fcfa8e3389ecc84a9309513_XAIN%20Legal%20Review%202020%20v1.pdf
  - platform: '[SyferText](https://github.com/OpenMined/SyferText)<br />[![Stars](https://img.shields.io/github/stars/OpenMined/SyferText.svg?color=blue)](https://github.com/OpenMined/SyferText/stargazers)<br
      />![](https://img.shields.io/github/last-commit/OpenMined/SyferText)'
    papers: ''
    affiliations: '[OpenMined](https://www.openmined.org/)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[EasyFL](https://github.com/EasyFL-AI/EasyFL)<br />[![Stars](https://img.shields.io/github/stars/EasyFL-AI/EasyFL.svg?color=blue)](https://github.com/EasyFL-AI/EasyFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/EasyFL-AI/EasyFL)'
    papers: '[EasyFL: A Low-code Federated Learning Platform For Dummies](https://ieeexplore.ieee.org/abstract/document/9684558)'
    affiliations: NTU
    sg: ''
    st: ''
    materials: {}
  - platform: '[FLSim](https://github.com/facebookresearch/FLSim)<br />[![Stars](https://img.shields.io/github/stars/facebookresearch/FLSim.svg?color=blue)](https://github.com/facebookresearch/FLSim/stargazers)<br
      />![](https://img.shields.io/github/last-commit/facebookresearch/FLSim)'
    papers: ''
    affiliations: '[facebook research ](https://github.com/facebookresearch)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[Breaching](https://github.com/JonasGeiping/breaching)<br />[![Stars](https://img.shields.io/github/stars/JonasGeiping/breaching.svg?color=blue)](https://github.com/JonasGeiping/breaching/stargazers)<br
      />![](https://img.shields.io/github/last-commit/JonasGeiping/breaching)'
    papers: A Framework for Attacks against Privacy in Federated Learning ([papers](https://github.com/JonasGeiping/breaching))
    affiliations: ''
    sg: ''
    st: ''
    materials: {}
  - platform: '[FedGraphNN](https://github.com/FedML-AI/FedGraphNN)<br />[![Stars](https://img.shields.io/github/stars/FedML-AI/FedGraphNN.svg?color=blue)](https://github.com/FedML-AI/FedGraphNN/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FedML-AI/FedGraphNN)'
    papers: '[FedGraphNN: A Federated Learning System and Benchmark for Graph Neural
      Networks](https://arxiv.org/abs/2104.07145)'
    affiliations: '[FedML](https://fedml.ai/)'
    sg: ':white_check_mark::white_check_mark:'
    st: ''
    materials: {}
  - platform: '[PyVertical ](https://github.com/OpenMined/PyVertical)<br />[![Stars](https://img.shields.io/github/stars/OpenMined/PyVertical.svg?color=blue)](https://github.com/OpenMined/PyVertical/stargazers)<br
      />![](https://img.shields.io/github/last-commit/OpenMined/PyVertical)'
    papers: '[PyVertical: A Vertical Federated Learning Framework for Multi-headed
      SplitNN](https://arxiv.org/abs/2104.00489.pdf)'
    affiliations: '[OpenMined](https://www.openmined.org/)'
    sg: ''
    st: ''
    materials: {}
  - platform: '[FLUTE](https://github.com/microsoft/msrflute)<br />[![Stars](https://img.shields.io/github/stars/microsoft/msrflute.svg?color=blue)](https://github.com/microsoft/msrflute/stargazers)<br
      />![](https://img.shields.io/github/last-commit/microsoft/msrflute)'
    papers: '[FLUTE: A Scalable, Extensible Framework for High-Performance Federated
      Learning Simulations](https://arxiv.org/abs/2203.13789)'
    affiliations: microsoft
    sg: ''
    st: ''
    materials:
      DOC: https://microsoft.github.io/msrflute/
  - platform: '[FedTorch](https://github.com/MLOPTPSU/FedTorch) <br />[![Stars](https://img.shields.io/github/stars/MLOPTPSU/FedTorch.svg?color=blue)](https://github.com/MLOPTPSU/FedTorch/stargazers)<br
      />![](https://img.shields.io/github/last-commit/MLOPTPSU/FedTorch)'
    papers: '[Distributionally Robust Federated Averaging](https://papers.nips.cc/paper/2020/file/ac450d10e166657ec8f93a1b65ca1b14-Paper.pdf)'
    affiliations: Penn State
    sg: ''
    st: ''
    materials: {}
  - platform: '[FLSim](https://github.com/iQua/flsim) <br />[![Stars](https://img.shields.io/github/stars/iQua/flsim.svg?color=blue)](https://github.com/iQua/flsim/stargazers)<br
      />![](https://img.shields.io/github/last-commit/iQua/flsim)'
    papers: '[Optimizing Federated Learning on Non-IID Data with Reinforcement Learning](https://ieeexplore.ieee.org/document/9155494/)'
    affiliations: University of Toronto
    sg: ''
    st: ''
    materials: {}
  - platform: '[PhotoLabeller](https://github.com/mccorby/PhotoLabeller)<br />[![Stars](https://img.shields.io/github/stars/mccorby/PhotoLabeller.svg?color=blue)](https://github.com/mccorby/PhotoLabeller/stargazers)<br
      />![](https://img.shields.io/github/last-commit/mccorby/PhotoLabeller)'
    papers: ''
    affiliations: ''
    sg: ''
    st: ''
    materials:
      BLOG: https://proandroiddev.com/federated-learning-e79e054c33ef
  - platform: '[FATE-Serving](https://github.com/FederatedAI/FATE-Serving) <br />[![Stars](https://img.shields.io/github/stars/FederatedAI/FATE-Serving.svg?color=blue)](https://github.com/FederatedAI/FATE-Serving/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FederatedAI/FATE-Serving)'
    papers: ''
    affiliations: '[WeBank](https://fedai.org/)'
    sg: ''
    st: ''
    materials:
      DOC: https://fate-serving.readthedocs.io/en/develop/
  - platform: '[PriMIA](https://github.com/gkaissis/PriMIA)<br />[![Stars](https://img.shields.io/github/stars/gkaissis/PriMIA.svg?color=blue)](https://github.com/gkaissis/PriMIA/stargazers)<br
      />![](https://img.shields.io/github/last-commit/gkaissis/PriMIA)'
    papers: '[End-to-end privacy preserving deep learning on multi-institutional medical
      imaging](https://www.nature.com/articles/s42256-021-00337-8)'
    affiliations: '[TUM](https://www.tum.de/en/); Imperial College London; [OpenMined](https://www.openmined.org)'
    sg: ''
    st: ''
    materials:
      DOC: https://g-k.ai/PriMIA/
  - platform: '[9nfl](https://github.com/jd-9n/9nfl)<br />[![Stars](https://img.shields.io/github/stars/jd-9n/9nfl.svg?color=blue)](https://github.com/jd-9n/9nfl/stargazers)<br
      />![](https://img.shields.io/github/last-commit/jd-9n/9nfl)'
    papers: ''
    affiliations: JD
    sg: ''
    st: ''
    materials: {}
  - platform: '[FedTree](https://github.com/Xtra-Computing/FedTree)<br />[![Stars](https://img.shields.io/github/stars/Xtra-Computing/FedTree.svg?color=blue)](https://github.com/Xtra-Computing/FedTree/stargazers)<br
      />![](https://img.shields.io/github/last-commit/Xtra-Computing/FedTree)'
    papers: ''
    affiliations: '[Xtra Computing Group](https://github.com/Xtra-Computing)'
    sg: ''
    st: ':white_check_mark::white_check_mark:'
    materials:
      DOC: https://fedtree.readthedocs.io/en/latest/index.html
  - platform: '[FedLearn](https://github.com/fedlearnAI/fedlearn-algo)<br />[![Stars](https://img.shields.io/github/stars/fedlearnAI/fedlearn-algo.svg?color=blue)](https://github.com/fedlearnAI/fedlearn-algo/stargazers)<br
      />![](https://img.shields.io/github/last-commit/fedlearnAI/fedlearn-algo)'
    papers: '[Fedlearn-Algo: A flexible open-source privacy-preserving machine learning
      platform](https://arxiv.org/abs/2107.04129)'
    affiliations: JD
    sg: ''
    st: ''
    materials: {}
  - platform: '[FEDn](https://github.com/scaleoutsystems/fedn)<br />[![Stars](https://img.shields.io/github/stars/scaleoutsystems/fedn.svg?color=blue)](https://github.com/scaleoutsystems/fedn/stargazers)<br
      />![](https://img.shields.io/github/last-commit/scaleoutsystems/fedn)'
    papers: '[Scalable federated machine learning with FEDn](https://arxiv.org/abs/2103.00148)'
    affiliations: '[scaleoutsystems](http://www.scaleoutsystems.com)'
    sg: ''
    st: ''
    materials:
      DOC: https://scaleoutsystems.github.io/fedn/
  - platform: '[FedCV](https://github.com/FedML-AI/FedCV)<br />[![Stars](https://img.shields.io/github/stars/FedML-AI/FedCV.svg?color=blue)](https://github.com/FedML-AI/FedCV/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FedML-AI/FedCV)'
    papers: '[FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://arxiv.org/abs/2111.11066)'
    affiliations: FedML
    sg: ''
    st: ''
    materials: {}
  - platform: '[FeTS](https://github.com/FETS-AI/Front-End)<br />[![Stars](https://img.shields.io/github/stars/FETS-AI/Front-End.svg?color=blue)](https://github.com/FETS-AI/Front-End/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FETS-AI/Front-End)'
    papers: '[The federated tumor segmentation (FeTS) tool: an open-source solution
      to further solid tumor research](http://iopscience.iop.org/article/10.1088/1361-6560/ac9449)'
    affiliations: '[Federated Tumor Segmentation (FeTS) initiative](https://www.med.upenn.edu/cbica/fets/)'
    sg: ''
    st: ''
    materials:
      DOC: https://fets-ai.github.io/Front-End/
  - platform: '[MPLC](https://github.com/LabeliaLabs/distributed-learning-contributivity)<br
      />[![Stars](https://img.shields.io/github/stars/LabeliaLabs/distributed-learning-contributivity.svg?color=blue)](https://github.com/LabeliaLabs/distributed-learning-contributivity/stargazers)<br
      />![](https://img.shields.io/github/last-commit/LabeliaLabs/distributed-learning-contributivity)'
    papers: ''
    affiliations: '[LabeliaLabs](https://github.com/LabeliaLabs)'
    sg: ''
    st: ''
    materials:
      PAGE: https://www.labelia.org
  - platform: '[UCADI](https://github.com/HUST-EIC-AI-LAB/UCADI) <br />[![Stars](https://img.shields.io/github/stars/HUST-EIC-AI-LAB/UCADI.svg?color=blue)](https://github.com/HUST-EIC-AI-LAB/UCADI/stargazers)<br
      />![](https://img.shields.io/github/last-commit/HUST-EIC-AI-LAB/UCADI)'
    papers: '[Advancing COVID-19 diagnosis with privacy-preserving collaboration in
      artificial intelligence](https://www.nature.com/articles/s42256-021-00421-z)'
    affiliations: Huazhong University of Science and Technology
    sg: ''
    st: ''
    materials: {}
  - platform: '[Flame](https://github.com/cisco-open/flame)<br />[![Stars](https://img.shields.io/github/stars/cisco-open/flame.svg?color=blue)](https://github.com/cisco-open/flame/stargazers)<br
      />![](https://img.shields.io/github/last-commit/cisco-open/flame)'
    papers: ''
    affiliations: Cisco
    sg: ''
    st: ''
    materials:
      DOC: https://fedsim.varnio.com/en/latest/
  - platform: '[APPFL](https://github.com/APPFL/APPFL)<br />[![Stars](https://img.shields.io/github/stars/APPFL/APPFL.svg?color=blue)](https://github.com/APPFL/APPFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/APPFL/APPFL)'
    papers: ''
    affiliations: ''
    sg: ''
    st: ''
    materials:
      DOC: https://appfl.readthedocs.io/en/stable/
  - platform: '[FlexCFL](https://github.com/morningD/FlexCFL)<br />[![Stars](https://img.shields.io/github/stars/morningD/FlexCFL.svg?color=blue)](https://github.com/morningD/FlexCFL/stargazers)<br
      />![](https://img.shields.io/github/last-commit/morningD/FlexCFL)'
    papers: '[Flexible Clustered Federated Learning for Client-Level Data Distribution
      Shift](https://arxiv.org/abs/2108.09749)'
    affiliations: Chongqing University
    sg: ''
    st: ''
    materials: {}
  - platform: '[OpenFed](https://github.com/FederalLab/OpenFed/)<br />[![Stars](https://img.shields.io/github/stars/FederalLab/OpenFed.svg?color=blue)](https://github.com/FederalLab/OpenFed/stargazers)<br
      />![](https://img.shields.io/github/last-commit/FederalLab/OpenFed)'
    papers: '[OpenFed: A Comprehensive and Versatile Open-Source Federated Learning
      Framework](https://arxiv.org/abs/2109.07852)'
    affiliations: ''
    sg: ''
    st: ''
    materials:
      DOC: https://openfed.readthedocs.io/README.html
  - platform: '[FedGroup](https://github.com/morningD/GrouProx)<br />[![Stars](https://img.shields.io/github/stars/morningD/GrouProx.svg?color=blue)](https://github.com/morningD/GrouProx/stargazers)<br
      />![](https://img.shields.io/github/last-commit/morningD/GrouProx)'
    papers: '[FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven
      Measure](https://arxiv.org/abs/2010.06870)'
    affiliations: Chongqing University
    sg: ''
    st: ''
    materials: {}
  - platform: '[FedEval](https://github.com/Di-Chai/FedEval)<br />[![Stars](https://img.shields.io/github/stars/Di-Chai/FedEval.svg?color=blue)](https://github.com/Di-Chai/FedEval/stargazers)<br
      />![](https://img.shields.io/github/last-commit/Di-Chai/FedEval)'
    papers: '[FedEval: A Benchmark System with a Comprehensive Evaluation Model for
      Federated Learning](https://arxiv.org/abs/2011.09655)'
    affiliations: HKU
    sg: ''
    st: ''
    materials:
      DOC: https://di-chai.github.io/FedEval/
  - platform: '[FedSim](https://github.com/varnio/fedsim)<br />[![Stars](https://img.shields.io/github/stars/varnio/fedsim.svg?color=blue)](https://github.com/varnio/fedsim/stargazers)<br
      />![](https://img.shields.io/github/last-commit/varnio/fedsim)'
    papers: ''
    affiliations: ''
    sg: ''
    st: ''
    materials: {}
  - platform: '[Federated-Learning-source](https://github.com/MTC-ETH/Federated-Learning-source)
      <br />[![Stars](https://img.shields.io/github/stars/MTC-ETH/Federated-Learning-source.svg?color=blue)](https://github.com/MTC-ETH/Federated-Learning-source/stargazers)<br
      />![](https://img.shields.io/github/last-commit/MTC-ETH/Federated-Learning-source)'
    papers: '[A Practical Federated Learning Framework for Small Number of Stakeholders](https://dl.acm.org/doi/10.1145/3437963.3441702)'
    affiliations: ETH Zürich
    sg: ''
    st: ''
    materials:
      DOC: https://github.com/MTC-ETH/Federated-Learning-source/blob/master/dashboard/README.md
  - platform: '[Clara](https://developer.nvidia.com/clara)'
    papers: ''
    affiliations: NVIDIA
    sg: ''
    st: ''
    materials: {}
  - platform: '[OpenHealth](https://github.com/QibingLee/OpenHealth) <br />[![Stars](https://img.shields.io/github/stars/QibingLee/OpenHealth.svg?color=blue)](https://github.com/QibingLee/OpenHealth/stargazers)<br
      />![](https://img.shields.io/github/last-commit/QibingLee/OpenHealth)'
    papers: ''
    affiliations: ZJU
    sg: ''
    st: ''
    materials: {}
fl-in-top-ir-conference-and-journal:
  header:
    title: 'Title                                                           '
    affiliation: '    Affiliation                        '
    venue: '    Venue    '
    year: '    Year    '
    materials: '    Materials'
  length:
    title: 60
    affiliation: 31
    venue: 5
    year: 4
    materials: 60
  body:
  - title: Is Non-IID Data a Threat in Federated Online Learning to Rank?
    affiliation: The University of Queensland
    venue: SIGIR
    year: '2022'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3477495.3531709
      CODE: https://github.com/ielab/2022-SIGIR-noniid-foltr
  - title: 'FedCT: Federated Collaborative Transfer for Recommendation'
    affiliation: Rutgers University
    venue: SIGIR
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3404835.3462825
      PDF: http://yongfeng.me/attach/liu-sigir2021.pdf
      CODE: https://github.com/CharlieMat/EdgeCDR
  - title: On the Privacy of Federated Pipelines
    affiliation: Technical University of Munich
    venue: SIGIR
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3404835.3462996
  - title: 'FedCMR: Federated Cross-Modal Retrieval.'
    affiliation: Dalian University of Technology
    venue: SIGIR
    year: '2021'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3404835.3462989
      CODE: https://github.com/hasakiXie123/FedCMR
  - title: Meta Matrix Factorization for Federated Rating Predictions.
    affiliation: SDU
    venue: SIGIR
    year: '2020'
    materials:
      PUB: https://dl.acm.org/doi/10.1145/3397271.3401081
      PDF: https://arxiv.org/abs/1910.10086