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.
@article{arxiv.2202.08402,
title = {Federated Stochastic Gradient Descent Begets Self-Induced Momentum},
author = {Howard H. Yang and Zuozhu Liu and Yaru Fu and Tony Q. S. Quek and H. Vincent Poor},
journal= {arXiv preprint arXiv:2202.08402},
year = {2022}
}