Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying

arXiv:2206.04742v2 Announce Type: replace
Abstract: 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 …

abstract algorithms arxiv asynchronous case communication convergence cs.ai cs.lg federated learning however issue mobile network paper server study systems type via world