Federated learning (FL) has garnered considerable attention due to its privacy-preserving feature. Nonetheless, the lack of freedom in managing user data can lead to group fairness issues, where models might be biased towards sensitive factors such as race or gender, even if they are trained using a legally compliant process. To redress this concern, this paper proposes a novel FL algorithm designed explicitly to address group fairness issues. We show empirically on CelebA and ImSitu datasets that the proposed method can improve fairness both quantitatively and qualitatively with minimal loss in accuracy in the presence of statistical heterogeneity and with different numbers of clients. Besides improving fairness, the proposed FL algorithm is compatible with local differential privacy (LDP), has negligible communication costs, and results in minimal overhead when migrating existing FL systems from the common FL protocol such as FederatedAveraging (FedAvg). We also provide the theoretical convergence rate guarantee for the proposed algorithm and the required noise level of the Gaussian mechanism to achieve desired LDP. This innovative approach holds significant potential to enhance the fairness and effectiveness of FL systems, particularly in sensitive applications such as healthcare or criminal justice.

翻译：联邦学习（FL）因其隐私保护特性而备受关注。然而，由于缺乏对用户数据的管理自由度，可能导致群体公平性问题——即便模型通过合法合规流程训练，仍可能对种族、性别等敏感因素产生偏见。为解决这一隐患，本文提出一种专用于处理群体公平性问题的新型FL算法。我们在CelebA和ImSitu数据集上的实验表明，所提方法能在统计异质性和不同客户端数量条件下，以最小精度损失实现公平性的定量与定性提升。除改善公平性外，该FL算法兼容本地差分隐私（LDP），具有可忽略的通信成本，且将现有FL系统从联邦平均（FedAvg）等常见协议迁移时仅需极低开销。我们还提供了所提算法的理论收敛速率保证，以及为实现目标LDP所需的高斯机制噪声水平。这一创新方法在医疗、刑事司法等敏感应用中具有显著提升FL系统公平性与有效性的潜力。