用基尼系数动态调节联邦学习公平性，提升弱势客户端表现
FedGA: A Fair Federated Learning Framework Based on the Gini Coefficient
- 以基尼系数衡量客户端性能差异，动态判断干预时机
- 根据实时公平状态调整聚合权重，优先吸纳低表现客户端信息
- 在多个数据集上显著降低基尼系数，兼顾公平与整体性能
公平性已成为联邦学习中的关键挑战。在水平联邦设置中，数据异构性常导致客户端间性能差异显著，引发模型行为不公的担忧。为此，我们提出 FedGA，一种面向公平性的联邦学习算法。首先，采用基尼系数衡量客户端间的性能差异，并建立基尼系数 $G$ 与全局模型更新幅度 $U_s$ 之间的关系，据此自适应确定公平性干预的时机。随后，基于系统实时公平状态动态调整聚合权重，使全局模型更充分融合表现较差客户端的信息。我们在 Office-Caltech-10、CIFAR-10 与 Synthetic 数据集上进行了大量实验。结果表明，FedGA 有效提升了方差和基尼系数等公平性指标，同时保持了较强的总体性能，验证了方法的有效性。
原文摘要 · Abstract (English)
Fairness has emerged as one of the key challenges in federated learning. In horizontal federated settings, data heterogeneity often leads to substantial performance disparities across clients, raising concerns about equitable model behavior. To address this issue, we propose FedGA, a fairness-aware federated learning algorithm. We first employ the Gini coefficient to measure the performance disparity among clients. Based on this, we establish a relationship between the Gini coefficient $G$ and the update scale of the global model ${U_s}$, and use this relationship to adaptively determine the timing of fairness intervention. Subsequently, we dynamically adjust the aggregation weights according to the system's real-time fairness status, enabling the global model to better incorporate information from clients with relatively poor performance.We conduct extensive experiments on the Office-Caltech-10, CIFAR-10, and Synthetic datasets. The results show that FedGA effectively improves fairness metrics such as variance and the Gini coefficient, while maintaining strong overall performance, demonstrating the effectiveness of our approach.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。