FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning

• URL: http://arxiv.org/abs/2506.03777v1
• Date: Wed, 04 Jun 2025 09:39:57 GMT
• Title: FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning
• Authors: Li Zhang, Zhongxuan Han, Chaochao chen, Xiaohua Feng, Jiaming Zhang, Yuyuan Li,
• Abstract summary: We propose a controllable group-fairness calibration framework, named FedFACT.<n>FedFACT identifies the Bayes-optimal classifiers under both global and local fairness constraints.<n>Experiments on multiple datasets demonstrate that FedFACT consistently outperforms baselines in balancing accuracy and global-local fairness.
• Score: 13.575259448363557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With emerging application of Federated Learning (FL) in decision-making scenarios, it is imperative to regulate model fairness to prevent disparities across sensitive groups (e.g., female, male). Current research predominantly focuses on two concepts of group fairness within FL: Global Fairness (overall model disparity across all clients) and Local Fairness (the disparity within each client). However, the non-decomposable, non-differentiable nature of fairness criteria pose two fundamental, unresolved challenges for fair FL: (i) Harmonizing global and local fairness in multi-class classification; (ii) Enabling a controllable, optimal accuracy-fairness trade-off. To tackle the aforementioned challenges, we propose a novel controllable federated group-fairness calibration framework, named FedFACT. FedFACT identifies the Bayes-optimal classifiers under both global and local fairness constraints in multi-class case, yielding models with minimal performance decline while guaranteeing fairness. To effectively realize an adjustable, optimal accuracy-fairness balance, we derive specific characterizations of the Bayes-optimal fair classifiers for reformulating fair FL as personalized cost-sensitive learning problem for in-processing, and bi-level optimization for post-processing. Theoretically, we provide convergence and generalization guarantees for FedFACT to approach the near-optimal accuracy under given fairness levels. Extensive experiments on multiple datasets across various data heterogeneity demonstrate that FedFACT consistently outperforms baselines in balancing accuracy and global-local fairness.

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