Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients that discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey that helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards FAFL.

翻译：联邦学习（FL）的最新进展为大规模分布式客户端带来了协作机器学习机遇，同时保障了性能与数据隐私。然而，当前多数研究聚焦于FL中央控制器的利益，忽视了FL客户端的诉求。这可能导致对客户端的不公平对待，削弱其参与学习过程的积极性，进而损害FL生态系统的可持续性。因此，确保FL公平性的课题正吸引着广泛的研究兴趣。近年来，为从不同维度实现FL公平性，学界提出了多种多样化的公平感知联邦学习（FAFL）方法。然而，目前尚缺乏能帮助读者全面认知这一交叉领域的系统性综述。本文旨在填补这一空白。通过梳理该领域现有文献所采用的基础性及简化性假设与公平性概念，我们提出涵盖FL关键环节（包括客户端选择、优化、贡献评估与激励分配）的FAFL方法分类体系。此外，我们讨论了实验评估FAFL方法性能的主要指标，并展望了FAFL领域具有潜力的未来研究方向。