FAT: Tilted Federated Learning with Alternating Direction Method of Multipliers

While federated learning (FL) has made significant strides in addressing data privacy concerns, the challenges of heterogeneous data and unfair performance among participants remain substantial. Existing solutions confront challenges such as high computational costs, difficulty in balancing performance with fairness, and poor convergence in partially data heterogeneous environments. The alternating direction method of multipliers (ADMM) is a highly promising approach that effectively addresses issues related to data heterogeneity by imposing constraints on local client updates through dual variables. In this paper, we propose a novel FL framework, named FAT (tilted FL with ADMM), designed to address the issue of data heterogeneity while reducing bias and unfair treatment towards different clients, and it provides a better trade-off between accuracy and fairness. We conducted experiments on two real-world datasets, and the results demonstrate that, compared to existing methods, FAT significantly improves fairness while maintaining accuracy. Our experiments demonstrate that FAT significantly outperforms existing state-of-the-art methods in both accuracy and fairness, offering a superior trade-off between these crucial aspects.