Scaff-PD: Communication Efficient Fair and Robust Federated Learning
Yaodong Yu Sai Praneeth Karimireddy Yi Ma Michael I. Jordan
We present Scaff-PD, a fast and communication-efficient algorithm for
distributionally robust federated learning. Our approach improves fairness by
optimizing a family of distributionally robust objectives tailored to
heterogeneous clients. We leverage the special structure of these objectives,
and design an accelerated primal dual (APD) algorithm which uses bias corrected
local steps (as in Scaffold) to achieve significant gains in communication
efficiency and convergence speed. We evaluate Scaff-PD on several benchmark
datasets and demonstrate its effectiveness in improving fairness and robustness
while maintaining competitive accuracy. Our results suggest that Scaff-PD is a
promising approach for federated learning in resource-constrained and
heterogeneous settings.