  Federated learning has a variety of applications in multiple domains by
utilizing private training data stored on different devices. However, the
aggregation process in federated learning is highly vulnerable to adversarial
attacks so that the global model may behave abnormally under attacks. To tackle
this challenge, we present a novel aggregation algorithm with residual-based
reweighting to defend federated learning. Our aggregation algorithm combines
repeated median regression with the reweighting scheme in iteratively
reweighted least squares. Our experiments show that our aggregation algorithm
outperforms other alternative algorithms in the presence of label-flipping and
backdoor attacks. We also provide theoretical analysis for our aggregation
algorithm.
