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We present Scaff-PD, a fast and communication-efficient algorithm fordistributionally robust federated learning. Our approach improves fairness byoptimizing a family of distributionally robust objectives tailored toheterogeneous clients. We leverage the special structure of these objectives,and design an accelerated primal dual (APD) algorithm which uses bias correctedlocal steps (as in Scaffold) to achieve significant gains in communicationefficiency and convergence speed. We evaluate Scaff-PD on several benchmarkdatasets and demonstrate its effectiveness in improving fairness and robustnesswhile maintaining competitive accuracy. Our results suggest that Scaff-PD is apromising approach for federated learning in resource-constrained andheterogeneous settings.

Scaff-PD: Communication Efficient Fair and Robust Federated Learning