Federated Learning (FL) is a distributed machine learning framework in which a set of local communities collaboratively learn a shared global model while retaining all training data locally within each community. Two notions of fairness have recently emerged as important issues for federated learning: group fairness and community fairness. Group fairness requires that a model's decisions do not favor any particular group based on a set of legally protected attributes such as race or gender. Community fairness requires that global models exhibit similar levels of performance (accuracy) across all collaborating communities. Both fairness concepts can coexist within an FL framework, but the existing literature has focused on either one concept or the other. This paper proposes and analyzes a post-processing fair federated learning (FFL) framework called post-FFL. Post-FFL uses a linear program to simultaneously enforce group and community fairness while maximizing the utility of the global model. Because Post-FFL is a post-processing approach, it can be used with existing FL training pipelines whose convergence properties are well understood. This paper uses post-FFL on real-world datasets to mimic how hospital networks, for example, use federated learning to deliver community health care. Theoretical results bound the accuracy lost when post-FFL enforces both notion of fairness. Experimental results illustrate that post-FFL simultaneously improves both group and community fairness in FL. Moreover, post-FFL outperforms the existing in-processing fair federated learning in terms of improving both notions of fairness, communication efficiency and computation cost.

翻译：联邦学习（FL）是一种分布式机器学习框架，其中一组本地社区协作学习共享的全局模型，同时将所有训练数据保留在各社区本地。近年来，两种公平性概念已成为联邦学习中的重要议题：群体公平性与社区公平性。群体公平性要求模型的决策不基于种族或性别等受法律保护的属性偏袒任何特定群体。社区公平性要求全局模型在所有协作社区间表现出相似水平的性能（准确率）。这两种公平性概念可在联邦学习框架中共存，但现有文献往往仅关注其中一种。本文提出并分析了一种称为后处理公平联邦学习（post-FFL）的后处理框架。post-FFL 通过线性规划同时强制执行群体与社区公平性，同时最大化全局模型的效用。由于 post-FFL 采用后处理方法，其可与收敛性已得到充分理解的现有联邦学习训练流程结合使用。本文在真实数据集上应用 post-FFL，以模拟医院网络等场景如何利用联邦学习提供社区医疗服务。理论分析结果界定了 post-FFL 在强制执行两种公平性时可能损失的准确率上限。实验结果表明，post-FFL 能同步提升联邦学习中的群体与社区公平性。此外，在改善两种公平性、通信效率及计算成本方面，post-FFL 均优于现有的处理中公平联邦学习方法。