Byzantine-Robust Federated Learning with Variance Reduction and
  Differential Privacy
Zikai Zhang Rui Hu
Federated learning (FL) is designed to preserve data privacy during model
training, where the data remains on the client side (i.e., IoT devices), and
only model updates of clients are shared iteratively for collaborative
learning. However, this process is vulnerable to privacy attacks and Byzantine
attacks: the local model updates shared throughout the FL network will leak
private information about the local training data, and they can also be
maliciously crafted by Byzantine attackers to disturb the learning. In this
paper, we propose a new FL scheme that guarantees rigorous privacy and
simultaneously enhances system robustness against Byzantine attacks. Our
approach introduces sparsification- and momentum-driven variance reduction into
the client-level differential privacy (DP) mechanism, to defend against
Byzantine attackers. The security design does not violate the privacy guarantee
of the client-level DP mechanism; hence, our approach achieves the same
client-level DP guarantee as the state-of-the-art. We conduct extensive
experiments on both IID and non-IID datasets and different tasks and evaluate
the performance of our approach against different Byzantine attacks by
comparing it with state-of-the-art defense methods. The results of our
experiments show the efficacy of our framework and demonstrate its ability to
improve system robustness against Byzantine attacks while achieving a strong
privacy guarantee.