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Federated Learning (FL) enables collaborative training without sharing raw data but often suffers fairness issues under non-IID distributions. Prior work targets client-level fairness yet overlooks demographic-group biases. We propose FedGF, a layer-wise method that embeds demographic-parity constraints into each layer’s descent direction, jointly optimizing accuracy, client fairness, and group fairness. Extensive experiments on benchmark datasets demonstrate that FedGF reduces group accuracy gaps by 78% compared to state-of-the-art methods while maintaining comparable model performance. Our method establishes new benchmarks for both client fairness (0.0862 fairness indicator on FMNIST) and group fairness (0.0002 demographic parity difference on CIFAR-10), highlighting its effectiveness in creating more equitable federated learning systems.

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FedGF: Layer-Wise Federated Learning with Group Fairness Guarantees

  • 会议论文

  • 发表日期：

    2025年

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  • Yu Huo,
  • Yating Li,
  • Xiaoying Tang

摘要

Federated Learning (FL) enables collaborative training without sharing raw data but often suffers fairness issues under non-IID distributions. Prior work targets client-level fairness yet overlooks demographic-group biases. We propose FedGF, a layer-wise method that embeds demographic-parity constraints into each layer’s descent direction, jointly optimizing accuracy, client fairness, and group fairness. Extensive experiments on benchmark datasets demonstrate that FedGF reduces group accuracy gaps by 78% compared to state-of-the-art methods while maintaining comparable model performance. Our method establishes new benchmarks for both client fairness (0.0862 fairness indicator on FMNIST) and group fairness (0.0002 demographic parity difference on CIFAR-10), highlighting its effectiveness in creating more equitable federated learning systems.