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Title: FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
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. Existing convergence results for gradient descent-based methods heavily rely on the fact that the gradient direction is used for updating. This paper presents a new class of convergence analysis for FL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to over-paramterized ReLU neural networks trained by gradient descent in FL and is inspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK converges to a global-optimal solution at a linear rate with properly tuned learning parameters. Furthermore, with proper distributional assumptions, FL-NTK can also achieve good generalization.  more » « less
Award ID(s):
2006359
NSF-PAR ID:
10258406
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
ArXivorg
Volume:
arXiv:2105
Issue:
arXiv:2105.05001
ISSN:
2331-8422
Page Range / eLocation ID:
1-40
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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