How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies.

翻译：如何确保公平性是联邦学习中的一个重要议题。近期研究探讨了如何根据客户端的贡献进行奖励（协作公平性），以及如何实现各客户端性能的一致性（性能公平性）。尽管在单个方面取得了进展，但我们认为同时考虑这两者至关重要，以吸引并激励更多多样化客户端参与联邦学习，从而获得高质量的全局模型。本文提出了一种同时优化两类公平性的新方法。具体而言，我们提出在梯度空间和数据空间中估计客户端贡献。在梯度空间中，我们监测每个客户端相对于其他客户端的梯度方向差异；在数据空间中，我们利用辅助模型测量客户端数据上的预测误差。基于这种贡献估计，我们提出了一种联邦学习方法——基于贡献估计的联邦训练（FedCE），即将估计结果作为全局模型聚合权重。我们对该方法进行了理论分析，并在两个真实医学数据集上进行了实证评估。实验验证了该方法在显著提升性能、改善协作公平性、增强性能公平性方面的有效性，并提供了全面的分析研究。