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

Xubo Yue (), Maher Nouiehed () and Raed Al Kontar ()
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Xubo Yue: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Maher Nouiehed: Industrial Engineering and Management, American University of Beirut, Beirut 1107-2020, Lebanon
Raed Al Kontar: Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109

INFORMS Joural on Data Science, 2023, vol. 2, issue 1, 10-23

Abstract: 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.

Keywords: federated data analytics; fairness; global model; personalized model; convergence (search for similar items in EconPapers)
Date: 2023
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