GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

• Published in: arXiv.org
• Main Authors: Yue, Xubo, Maher Nouiehed, Raed Al Kontar
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.03.2022
• Subjects: Algorithms; Computer Science - Distributed, Parallel, and Cluster Computing; Computer Science - Learning; Convergence; Image classification; Machine learning; Regularization
• DOI: 10.48550/arxiv.2108.02741

In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework \texttt{GIFAIR-FL} can accommodate both global and personalized settings. Theoretically, we show convergence in non-convex and strongly convex settings. Our convergence guarantees hold for both \(i.i.d.\) and non-\(i.i.d.\) data. To demonstrate the empirical performance of our algorithm, we apply our method to image classification and text prediction tasks. Compared to existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy.