意想不到的朋友：通过聚类增强联邦学习中的局部公平性
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
AI总结：
针对异构联邦学习中全局与局部公平性不一致的问题，提出基于聚类的局部公平FL新算法，通过引入新公平性度量实现局部准确率与公平性的可调平衡，无需显式干预即可达到或超越现有方法。
摘要：
联邦学习（FL）一直是跨分布式数据集协同训练机器学习模型的关键范式。在异构环境中，观察到单一共享的FL模型会导致较低的局部准确率，从而推动了个性化FL算法的发展。与此同时，公平FL算法被提出以在全局模型上实施群体公平性。同样，在异构环境中，全局公平性和局部公平性不一定一致，这推动了近期关于局部公平FL的研究。在本文中，我们针对异构环境提出了新的FL算法，涵盖了个性化FL与局部公平FL之间的空间。基于现有的基于聚类的个性化FL方法，我们将一种新的公平性度量纳入聚类分配中，从而实现局部准确率与公平性之间可调节的平衡。我们的方法在没有显式公平性干预的情况下，匹配或超越了现有局部公平FL方法的性能。我们进一步（通过数值和理论分析）证明，仅个性化就能提高局部公平性，并且当这种一致性存在时，我们的方法能够利用这种一致性。
英文摘要：
Federated Learning (FL) has been a pivotal paradigm for collaborative training of machine learning models across distributed datasets. In heterogeneous settings, it has been observed that a single shared FL model can lead to low local accuracy, motivating personalized FL algorithms. In parallel, fair FL algorithms have been proposed to enforce group fairness on the global models. Again, in heterogeneous settings, global and local fairness do not necessarily align, motivating the recent literature on locally fair FL. In this paper, we propose new FL algorithms for heterogeneous settings, spanning the space between personalized and locally fair FL. Building on existing clustering-based personalized FL methods, we incorporate a new fairness metric into cluster assignment, enabling a tunable balance between local accuracy and fairness. Our methods match or exceed the performance of existing locally fair FL approaches, without explicit fairness intervention. We further demonstrate (numerically and analytically) that personalization alone can improve local fairness and that our methods exploit this alignment when present.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
