系统梳理联邦学习中的公平性问题与解决方法
Federated Learning at the Forefront of Fairness: A Multifaceted Perspective
- 从性能与能力双视角分类公平性方法
- 提出框架以平衡公平性与模型性能
- 适合关注公平性、伦理与算法设计的研究者
联邦学习中的公平性正日益成为关键议题，受异构客户端约束及多场景下模型性能均衡需求驱动。本文从多维度视角系统梳理当前主流的公平性感知方法，分为以模型性能为导向和以能力为导向两类。我们构建了一个框架，用于分类和应对各类公平性关切及其技术实现，评估其在联邦学习架构中平衡公平与性能的有效性。同时，探讨了若干重要量化评估指标。最后，展望了前沿开放问题，并提出可能推动该领域发展的前瞻性解决方案，为致力于联邦学习公平性的研究者奠定坚实基础。
原文摘要 · Abstract (English)
Fairness in Federated Learning (FL) is emerging as a critical factor driven by heterogeneous clients' constraints and balanced model performance across various scenarios. In this survey, we delineate a comprehensive classification of the state-of-the-art fairness-aware approaches from a multifaceted perspective, i.e., model performance-oriented and capability-oriented. Moreover, we provide a framework to categorize and address various fairness concerns and associated technical aspects, examining their effectiveness in balancing equity and performance within FL frameworks. We further examine several significant evaluation metrics leveraged to measure fairness quantitatively. Finally, we explore exciting open research directions and propose prospective solutions that could drive future advancements in this important area, laying a solid foundation for researchers working toward fairness in FL.
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