Mitigating Group Bias in Federated Learning: Beyond Local Fairness
Abstract: The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is privately stored on multiple clients. To address this, many proposals try to mitigate bias at the level of clients before aggregation, which we call locally fair training. However, the effectiveness of these approaches is not well understood. In this work, we investigate the theoretical foundation of locally fair training by studying the relationship between global model fairness and local model fairness. Additionally, we prove that for a broad class of fairness metrics, the global model's fairness can be obtained using only summary statistics from local clients. Based on that, we propose a globally fair training algorithm that optimizes the fairness-regularized empirical loss. Real-data experiments demonstrate the promising performance of our proposed approach for enhancing fairness
Submission Length: Regular submission (no more than 12 pages of main content)
Assigned Action Editor: ~Ahmad_Beirami1
Submission Number: 2690
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