Abstract
This paper studies asynchronous Federated Learning (FL) subject to clients'
individual arbitrary communication patterns with the parameter server. We
propose FedMobile, a new asynchronous FL algorithm that exploits the mobility
attribute of the mobile FL system to improve the learning performance. The key
idea is to leverage the random client-to-client communication in a mobile
network to create additional indirect communication opportunities with the
server via upload and download relaying. We prove that FedMobile achieves a
convergence rate $O(\frac{1}{\sqrt{NT}})$, where $N$ is the number of clients
and $T$ is the number of communication slots, and show that the optimal design
involves an interesting trade-off on the best timing of relaying. Our analysis
suggests that with an increased level of mobility, asynchronous FL converges
faster using FedMobile. Experiment results on a synthetic dataset and two
real-world datasets verify our theoretical findings.