  Federated learning has emerged as a promising, massively distributed way to
train a joint deep model over large amounts of edge devices while keeping
private user data strictly on device. In this work, motivated from ensuring
fairness among users and robustness against malicious adversaries, we formulate
federated learning as multi-objective optimization and propose a new algorithm
FedMGDA+ that is guaranteed to converge to Pareto stationary solutions.
FedMGDA+ is simple to implement, has fewer hyperparameters to tune, and
refrains from sacrificing the performance of any participating user. We
establish the convergence properties of FedMGDA+ and point out its connections
to existing approaches. Extensive experiments on a variety of datasets confirm
that FedMGDA+ compares favorably against state-of-the-art.

Machine Learning;Machine Learning