Federated Triplet Loss

Paper: Augenstein, Sean, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Aguera y Arcas. 2019. “Generative Models for Effective ML on Private, Decentralized Datasets”

Demos

Ablation for differentially private federated GAN

• ./demos/federated-gan-training-loop.ipynb: training a federated GAN on Colab
• ./demos/ablation-results.ipynb: visualizing the runs

Examples of successful convergence after 1000 rounds

• dp_l2_norm_clip: 0.1, dp_noise_multiplier: 0.01

  • Sample:

    

  • Log:

    

• dp_l2_norm_clip: 0.08036, dp_noise_multiplier: 0.00555

  • Sample:

    

  • Log:

    

• dp_l2_norm_clip: 0.105667, dp_noise_multiplier: 0.001130

  • Sample

    

  • Log

    

Examples of unsuccessful convergence after 1000 rounds

• dp_l2_norm_clip: 0.119396, dp_noise_multiplier: 0.064232

  • Sample

    

  • Log

    

• dp_l2_norm_clip: 2.408976, dp_noise_multiplier: 0.386463

  • Sample

    

    The model diverged early in the process:

    • Round 120

      

    • Round 130

      

  • Log