  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.
