2022/06/09 by Jieming Bian, Jie Xu, Bian, Jieming +1 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #Caching and Content Delivery #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.2206.04742
openalex publication_date 2022/06/09 · openalex created_date 2022/06/14 · openalex updated_date 2026/07/28
This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the server is always available, however, this is not the case in many real-world systems. To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL. By exploiting mobility, the study shows that clients can indirectly communicate with the server through another client serving as a relay, creating additional communication opportunities. This enables clients to upload local model updates sooner or receive fresher global models. We propose a new FL algorithm, called FedMobile, that incorporates opportunistic relaying and addresses key questions such as when and how to relay. 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. The paper also presents an extension that considers data manipulation before relaying to reduce the cost and enhance privacy. Experiment results on a synthetic dataset and two real-world datasets verify our theoretical findings.