  Federated learning (FL) is a popular distributed learning framework that
trains a global model through iterative communications between a central server
and edge devices. Recent works have demonstrated that FL is vulnerable to model
poisoning attacks. Several server-based defense approaches (e.g. robust
aggregation), have been proposed to mitigate such attacks. However, we
empirically show that under extremely strong attacks, these defensive methods
fail to guarantee the robustness of FL. More importantly, we observe that as
long as the global model is polluted, the impact of attacks on the global model
will remain in subsequent rounds even if there are no subsequent attacks. In
this work, we propose a client-based defense, named White Blood Cell for
Federated Learning (FL-WBC), which can mitigate model poisoning attacks that
have already polluted the global model. The key idea of FL-WBC is to identify
the parameter space where long-lasting attack effect on parameters resides and
perturb that space during local training. Furthermore, we derive a certified
robustness guarantee against model poisoning attacks and a convergence
guarantee to FedAvg after applying our FL-WBC. We conduct experiments on
FasionMNIST and CIFAR10 to evaluate the defense against state-of-the-art model
poisoning attacks. The results demonstrate that our method can effectively
mitigate model poisoning attack impact on the global model within 5
communication rounds with nearly no accuracy drop under both IID and Non-IID
settings. Our defense is also complementary to existing server-based robust
aggregation approaches and can further improve the robustness of FL under
extremely strong attacks.
