A Huber Loss Minimization Approach to Byzantine Robust Federated Learning

Abstract

Federated learning systems are susceptible to adversarial attacks. To combat this, we introduce a novel aggregator based on Huber loss minimization, and provide a comprehensive theoretical analysis. Under independent and identically distributed (i.i.d) assumption, our approach has several advantages compared to existing methods. Firstly, it has optimal dependence on $ϵ$, which stands for the ratio of attacked clients. Secondly, our approach does not need precise knowledge of $ϵ$. Thirdly, it allows different clients to have unequal data sizes. We then broaden our analysis to include non-i.i.d data, such that clients have slightly different distributions.