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.

本文探讨了异步联邦学习中客户端个体任意通信模式对参数服务器的影响。我们提出了 FedMobile，这是一种利用移动联邦系统的便利性来提高学习性能的新型异步 FL 算法，并且验证了我们的理论发现。