Robust Federated Learning with Noisy Communication

Abstract--Federated learning is a communication-efficient training process that alternates between local training at the edge devices and averaging the updated local model at the central server . Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication d ue to noise, which also brings serious effects on federated learn ing. T o tackle this challenge, we propose a robust design for federa ted learning to alleviate the effects of noise in this paper . Con sidering noise in the two aforementioned steps, we first formulate the training problem as a parallel optimization for each node un der the expectation-based model and the worst-case model. Due t o the non-convexity of the problem, a regularization for the l oss function approximation method is proposed to make it tracta ble. Regarding the worst-case model, we develop a feasible train ing scheme which utilizes the sampling-based successive conve x approximation algorithm to tackle the unavailable maxima o r minima noise condition and the non-convex issue of the objec tive function. Furthermore, the convergence rates of both new de signs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of los s function are demonstrated via simulations for the proposed designs. UTURE wireless computing applications demand higher bandwidth, lower latency and more reliable connections with numerous devices [1].