  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 $\epsilon$, which
stands for the ratio of attacked clients. Secondly, our approach does not need
precise knowledge of $\epsilon$. 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.
