Mobility Improves the Convergence of Asynchronous Federated Learning
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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(1/√(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.
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