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We present a class of methods for robust, personalized federated learning,called Fed+, that unifies many federated learning algorithms. The principaladvantage of this class of methods is to better accommodate the real-worldcharacteristics found in federated training, such as the lack of IID dataacross parties, the need for robustness to outliers or stragglers, and therequirement to perform well on party-specific datasets. We achieve this througha problem formulation that allows the central server to employ robust ways ofaggregating the local models while keeping the structure of local computationintact. Without making any statistical assumption on the degree ofheterogeneity of local data across parties, we provide convergence guaranteesfor Fed+ for convex and non-convex loss functions under different (robust)aggregation methods. The Fed+ theory is also equipped to handle heterogeneouscomputing environments including stragglers without additional assumptions;specifically, the convergence results cover the general setting where thenumber of local update steps across parties can vary. We demonstrate thebenefits of Fed+ through extensive experiments across standard benchmarkdatasets.

Robustness and Personalization in Federated Learning: A Unified Approach via Regularization