  Federated learning enables learning from decentralized data sources without
compromising privacy, which makes it a crucial technique. However, it is
vulnerable to model poisoning attacks, where malicious clients interfere with
the training process. Previous defense mechanisms have focused on the
server-side by using careful model aggregation, but this may not be effective
when the data is not identically distributed or when attackers can access the
information of benign clients. In this paper, we propose a new defense
mechanism that focuses on the client-side, called FedDefender, to help benign
clients train robust local models and avoid the adverse impact of malicious
model updates from attackers, even when a server-side defense cannot identify
or remove adversaries. Our method consists of two main components: (1)
attack-tolerant local meta update and (2) attack-tolerant global knowledge
distillation. These components are used to find noise-resilient model
parameters while accurately extracting knowledge from a potentially corrupted
global model. Our client-side defense strategy has a flexible structure and can
work in conjunction with any existing server-side strategies. Evaluations of
real-world scenarios across multiple datasets show that the proposed method
enhances the robustness of federated learning against model poisoning attacks.
