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

In this paper, we propose GIFAIR-FL, a framework that imposes group and individual fairness (GIFAIR) to federated learning (FL) 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 GIFAIR-FL can accommodate both global and personalized settings. Theoretically, we show convergence in nonconvex and strongly convex settings. Our convergence guarantees hold for both independent and identically distributed (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 with existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy. History: Kwok-Leung Tsui served as the senior editor for this article. Funding: This work was supported by NSF CAREER Grant 2144147. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/2590027/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0022 ).