Federated Stochastic Gradient Descent Begets Self-Induced Momentum

• URL: http://arxiv.org/abs/2202.08402v1
• Date: Thu, 17 Feb 2022 02:01:37 GMT
• Title: Federated Stochastic Gradient Descent Begets Self-Induced Momentum
• Authors: Howard H. Yang, Zuozhu Liu, Yaru Fu, Tony Q. S. Quek, H. Vincent Poor
• Abstract summary: Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems. We show that running to the gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process.
• Score: 151.4322255230084
• License: http://creativecommons.org/licenses/by/4.0/
• 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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