  Federated Learning (FL) enables multiple distributed clients (e.g., mobile
devices) to collaboratively train a centralized model while keeping the
training data locally on the client. Compared to traditional centralized
machine learning, FL offers many favorable features such as offloading
operations which would usually be performed by a central server and reducing
risks of serious privacy leakage. However, Byzantine clients that send
incorrect or disruptive updates due to system failures or adversarial attacks
may disturb the joint learning process, consequently degrading the performance
of the resulting model. In this paper, we propose to mitigate these failures
and attacks from a spatial-temporal perspective. Specifically, we use a
clustering-based method to detect and exclude incorrect updates by leveraging
their geometric properties in the parameter space. Moreover, to further handle
malicious clients with time-varying behaviors, we propose to adaptively adjust
the learning rate according to momentum-based update speculation. Extensive
experiments on 4 public datasets demonstrate that our algorithm achieves
enhanced robustness comparing to existing methods under both cross-silo and
cross-device FL settings with faulty/malicious clients.
