  We establish a connection between federated learning, a concept from machine
learning, and mean-field games, a concept from game theory and control theory.
In this analogy, the local federated learners are considered as the players and
the aggregation of the gradients in a central server is the mean-field effect.
We present federated learning as a differential game and discuss the properties
of the equilibrium of this game. We hope this novel view to federated learning
brings together researchers from these two distinct areas to work on
fundamental problems of large-scale distributed and privacy-preserving learning
algorithms.
