optazur 5

content/_index.md

@@ -12,19 +12,22 @@ OptAzur organizes a monthly seminar in Nice and Sophia-Antipolis, which alternat
 
 ### Next talk
 
-Monday, December 18th, 2023 (I3S, Sophia)
+Monday, January 15th, 2024 (LJAD, Nice)
 
-14h - [Maurizio Filippone](https://www.eurecom.fr/~filippon/) (EURECOM)
+14h - [Marco Lorenzi](https://marcolorenzi.github.io) (Inria)
 
-**One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models**
+**Federated Learning in Healthcare Applications: from Theory to Practice**
 
-Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sample quality compared to state-of-the-art score-based diffusion models (DMs). This paper provides a significant step in the direction of addressing this limitation. The idea is to borrow one of the strengths of score-based DMs, which is the ability to perform accurate density estimation in low-density regions and to address manifold overfitting by means of data mollification. We propose a view of data mollification within likelihood-based GMs as a continuation method, whereby the optimization objective smoothly transitions from simple-to-optimize to the original target. Crucially, data mollification can be implemented by adding one line of code in the optimization loop, and I will show that this provides a boost in generation quality of likelihood-based GMs, without computational overheads. I will then present results on real-world image data sets and UCI benchmarks with popular likelihood-based GMs, including variants of variational autoencoders and normalizing flows, showing large improvements in FID score and density estimation.
+This talk will illustrate current advances in federated learning (FL) for sensitive applications, from a both theoretical and practical perspectives. 
+In spite of the wide interest in the federated learning paradigm, current applications to sensitive domains, such as healthcare, are still challenging due to the complexity in dealing with heterogeneous and complex data hosted in different hospitals, as well as to the practical difficulty of deploying federated architectures in the real world.
+I will first cover a novel theory for modeling the impact of clients heterogeneity on the convergence guarantees of federated learning [1, 2]. In particular, I will study the robustness and variability of federated learning to heterogeneous conditions, by introducing the notion of stochastic aggregation weights. The proposed framework allows to derive novel federated optimization schemes to maximise the representativity and minimize the variability of clients contributions across federated optimization rounds.
+I will also introduce a novel perspective to Federated Unlearning (FU), a novel FL problem aiming at providing theoretical guarantees on the removal of the contribution of a given client from a federated training procedure. Upon unlearning request from a given client, FU is based on the definition of criteria to identify the optimal FL iteration from which FL has to be reinitialized, along with randomized perturbation mechanism to provide unlearning guarantees [3]. 
+Finally, from the practical standpoint, the talk will introduce Fed-BioMed, a development initiative aiming at translating federated learning to healthcare applications [4]. Fed-BioMed tackles the challenges required to meet real-world translation, concerning FL security, scalability and interoperability. I will give an illustration of the interplay between methodological development and translational effort that characterise the development of the Fed-BioMed FL platform, and discuss our current effort in delivering FL in hospitals networks. 
+[1] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Clustered sampling: Low-variance and improved representativity for clients selection in federated learning., In International Conference on Machine Learning (pp. 3407-3416). PMLR. 
+[2] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates., Journal of Machine Learning Research (110):1-43, 2023.
+[3] Fraboni Y., Vidal R., Kameni L. and Lorenzi M. Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization., arXiv preprint arXiv:2211.11656.
+[4] Cremonesi F., et al.Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications, White paper, arXiv:2304.12012.
 
-15h15 - [Yassine Laguel](https://yassine-laguel.github.io) (LJAD, Nice)
-
-**High Probability and Risk-Averse Guarantees for Stochastic Saddle Point Problems**
-
-We investigate the stochastic accelerated primal-dual algorithm for strongly-convex-strongly-concave saddle point problems, common in distributionally robust learning, game theory, and fairness in machine learning. Our algorithm offers optimal complexity in several settings and we provide high probability guarantees for convergence to a neighborhood of the saddle point. We derive analytical formulas for the limit covariance matrix and develop lower bounds to show that our analysis is tight. Our risk- averse convergence analysis characterizes the trade-offs between bias and risk in approximate solutions. We present numerical experiments on zero-sum games and robust learning problems.
 
 ### Previous talks
 
@@ -33,6 +36,7 @@ Titles and abstracts [here](/previous)
 - #1: [Jean-François Aujol](https://www.math.u-bordeaux.fr/~jaujol/) (Université de Bordeaux) and [Luca Calatroni](https://sites.google.com/view/lucacalatroni/home) (CNRS, I3S)
 - #2: [Gersende Fort](https://perso.math.univ-toulouse.fr/gfort/) (CNRS, Institut de Mathématiques de Toulouse) and [Samuel Vaiter](https://samuelvaiter.com) (CNRS, Laboratoire J. A. Dieudonné)
 - #3: [Massimiliano Pontil](https://www.iit.it/people-details/-/people/massimiliano-pontil) (Italian Institute of Technology and University College London) and [Mathieu Carrière](https://www-sop.inria.fr/members/Mathieu.Carriere/) (Inria)
+- #4: [Maurizio Filippone](https://www.eurecom.fr/~filippon/) (EURECOM) and [Yassine Laguel](https://yassine-laguel.github.io) (Laboratoire J. A. Dieudonné)
 
 ## Events
 

content/previous.md

@@ -1,5 +1,19 @@
 # Previous talks @OptAzur
 
+## OptAzur #4 -- Monday, December 18th, 2023 (Sophia)
+
+14h - [Maurizio Filippone](https://www.eurecom.fr/~filippon/) (EURECOM)
+
+**One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models**
+
+Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due to the possibility to generate new data by a single model evaluation. However, they typically achieve lower sample quality compared to state-of-the-art score-based diffusion models (DMs). This paper provides a significant step in the direction of addressing this limitation. The idea is to borrow one of the strengths of score-based DMs, which is the ability to perform accurate density estimation in low-density regions and to address manifold overfitting by means of data mollification. We propose a view of data mollification within likelihood-based GMs as a continuation method, whereby the optimization objective smoothly transitions from simple-to-optimize to the original target. Crucially, data mollification can be implemented by adding one line of code in the optimization loop, and I will show that this provides a boost in generation quality of likelihood-based GMs, without computational overheads. I will then present results on real-world image data sets and UCI benchmarks with popular likelihood-based GMs, including variants of variational autoencoders and normalizing flows, showing large improvements in FID score and density estimation.
+
+15h15 - [Yassine Laguel](https://yassine-laguel.github.io) (LJAD, Nice)
+
+**High Probability and Risk-Averse Guarantees for Stochastic Saddle Point Problems**
+
+We investigate the stochastic accelerated primal-dual algorithm for strongly-convex-strongly-concave saddle point problems, common in distributionally robust learning, game theory, and fairness in machine learning. Our algorithm offers optimal complexity in several settings and we provide high probability guarantees for convergence to a neighborhood of the saddle point. We derive analytical formulas for the limit covariance matrix and develop lower bounds to show that our analysis is tight. Our risk- averse convergence analysis characterizes the trade-offs between bias and risk in approximate solutions. We present numerical experiments on zero-sum games and robust learning problems.
+
 ## OptAzur #3 -- Monday, November 20th, 2023 (Nice)
 
 14h - [Massimiliano Pontil](https://www.iit.it/people-details/-/people/massimiliano-pontil) (Italian Institute of Technology and University College London)