FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis

Understand

Federated Learning (FL) is an emerging learning scheme that allows different distributed clients to train deep neural networks together without data sharing.

• Neural networks have become popular due to their unprecedented success.
• To the best of our knowledge, the theoretical guarantees of FL concerning neural networks with explicit forms and multi-step updates are unexplored.
• Nevertheless, training analysis of neural networks in FL is non-trivial for two reasons: first, the objective loss function we are optimizing is non-smooth and non-convex, and second, we are even not updating in the gradient direction.