Robust Federated Learning with Noisy Communication

TL;DR

This paper introduces a robust federated learning framework that effectively mitigates the impact of communication noise, enhancing training stability and accuracy in wireless environments.

Contribution

It develops novel optimization schemes for noisy federated learning considering both expectation-based and worst-case noise models, with theoretical convergence analysis.

Findings

Improved prediction accuracy in noisy communication scenarios

Reduced loss function compared to baseline methods

Validated effectiveness through simulations

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 due to noise, which also brings serious effects on federated learning. To tackle this challenge, we propose a robust design for federated learning to alleviate the effects of noise in this paper. Considering noise in the two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and the worst-case model. Due to the non-convexity of the problem, a regularization for the loss function approximation method is proposed to make it tractable. Regarding the worst-case model, we develop a feasible training scheme which utilizes the sampling-based successive convex approximation algorithm to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function are demonstrated via simulations for the proposed designs.