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Federated learning (FL) is an emerging machine learning method that can beapplied in mobile edge systems, in which a server and a host of clientscollaboratively train a statistical model utilizing the data and computationresources of the clients without directly exposing their privacy-sensitivedata. We show that running stochastic gradient descent (SGD) in such a settingcan 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 federatedlearning system by accounting for the effects of parameter staleness andcommunication resources. These results advance the understanding of theFederated SGD algorithm, and also forges a link between staleness analysis andfederated computing systems, which can be useful for systems designers.

Federated Stochastic Gradient Descent Begets Self-Induced Momentum