Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federated learned model tends to be biased against certain demographic groups (e.g., racial and gender groups) due to the inherent FL properties, such as data heterogeneity and party selection. Unlike centralized learning, mitigating bias in FL is particularly challenging as private training datasets and their sensitive attributes are typically not directly accessible. Most prior research in this field only focuses on global fairness while overlooking the local fairness of individual clients. Moreover, existing methods often require sensitive information about the client's local datasets to be shared, which is not desirable. To address these issues, we propose GLOCALFAIR, a client-server co-design fairness framework that can jointly improve global and local group fairness in FL without the need for sensitive statistics about the client's private datasets. Specifically, we utilize constrained optimization to enforce local fairness on the client side and adopt a fairness-aware clustering-based aggregation on the server to further ensure the global model fairness across different sensitive groups while maintaining high utility. Experiments on two image datasets and one tabular dataset with various state-of-the-art fairness baselines show that GLOCALFAIR can achieve enhanced fairness under both global and local data distributions while maintaining a good level of utility and client fairness.

翻译：联邦学习（FL）已成为一种在不牺牲数据隐私前提下跨客户端协作训练共享模型的前瞻性解决方案。然而，受数据异质性和参与方选择等FL固有特性影响，联邦学习模型往往对特定人口群体（如种族和性别群体）存在偏见。与集中式学习不同，由于私有训练数据集及其敏感属性通常无法直接访问，在FL中缓解偏见尤为困难。该领域现有研究大多仅关注全局公平性，而忽视了单个客户端的局部公平性。此外，现有方法通常需要共享客户端本地数据集的敏感信息，这并不理想。针对这些问题，我们提出GLOCALFAIR——一种客户端-服务器协同设计的公平性框架，可在无需客户端私有数据集敏感统计信息的前提下，共同提升FL中的全局与局部群体公平性。具体而言，我们利用约束优化在客户端侧强制执行局部公平性，并采用基于公平性感知的聚类聚合方法在服务器端进一步确保跨不同敏感分组的全局模型公平性，同时保持高效用性。在两个图像数据集和一个表格数据集上，基于多种最先进的公平性基线的实验表明，GLOCALFAIR能够在维持良好效用水平与客户端公平性的同时，在全局和局部数据分布下实现更优的公平性。