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.

翻译：我们提出了Scaff-PD，一种面向分布鲁棒联邦学习的快速且通信高效的算法。该方法通过优化针对异构客户端定制的分布鲁棒目标族来提升公平性。我们利用这些目标的特殊结构，设计了一种加速原始-对偶（APD）算法，该算法采用（如Scaffold中的）偏差修正局部步骤，在通信效率和收敛速度上取得了显著提升。我们在多个基准数据集上评估了Scaff-PD，并证明了其在保持竞争性精度的同时，在改善公平性和鲁棒性方面的有效性。结果表明，Scaff-PD是一种适用于资源受限与异构场景的联邦学习有前景方法。