2022/04/15 by Howard H. Yang, Zihan Chen, Yang, Howard H. +3
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #Systems and Control (eess.SY) #Wireless Communication Security Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2204.07609
openalex publication_date 2022/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We demonstrate that merely analog transmissions and match filtering can realize the function of an edge server in federated learning (FL). Therefore, a network with massively distributed user equipments (UEs) can achieve large-scale FL without an edge server. We also develop a training algorithm that allows UEs to continuously perform local computing without being interrupted by the global parameter uploading, which exploits the full potential of UEs' processing power. We derive convergence rates for the proposed schemes to quantify their training efficiency. The analyses reveal that when the interference obeys a Gaussian distribution, the proposed algorithm retrieves the convergence rate of a server-based FL. But if the interference distribution is heavy-tailed, then the heavier the tail, the slower the algorithm converges. Nonetheless, the system run time can be largely reduced by enabling computation in parallel with communication, whereas the gain is particularly pronounced when communication latency is high. These findings are corroborated via excessive simulations.