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arXiv · 2202.08402

Federated Stochastic Gradient Descent Begets Self-Induced Momentum

Howard H. Yang·Zuozhu Liu·Yaru Fu·Tony Q. S. Quek·H. Vincent Poor
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Abstract

Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data. We show that running stochastic gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process. Based on this finding, we further analyze the convergence rate of a federated learning system by accounting for the effects of parameter staleness and communication resources. These results advance the understanding of the Federated SGD algorithm, and also forges a link between staleness analysis and federated computing systems, which can be useful for systems designers.

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Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor. 2022-02-17. Federated Stochastic Gradient Descent Begets Self-Induced Momentum. https://arxiv.org/abs/2202.08402

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