  Federated learning provides a communication-efficient and privacy-preserving
training process by enabling learning statistical models with massive
participants while keeping their data in local clients. However, standard
federated learning techniques that naively minimize an average loss function
are vulnerable to data corruptions from outliers, systematic mislabeling, or
even adversaries. In addition, it is often prohibited for service providers to
verify the quality of data samples due to the increasing concern of user data
privacy. In this paper, we address this challenge by proposing Auto-weighted
Robust Federated Learning (arfl), a novel approach that jointly learns the
global model and the weights of local updates to provide robustness against
corrupted data sources. We prove a learning bound on the expected risk with
respect to the predictor and the weights of clients, which guides the
definition of the objective for robust federated learning. The weights are
allocated by comparing the empirical loss of a client with the average loss of
the best p clients (p-average), thus we can downweight the clients with
significantly high losses, thereby lower their contributions to the global
model. We show that this approach achieves robustness when the data of
corrupted clients is distributed differently from benign ones. To optimize the
objective function, we propose a communication-efficient algorithm based on the
blockwise minimization paradigm. We conduct experiments on multiple benchmark
datasets, including CIFAR-10, FEMNIST and Shakespeare, considering different
deep neural network models. The results show that our solution is robust
against different scenarios including label shuffling, label flipping and noisy
features, and outperforms the state-of-the-art methods in most scenarios.
