Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL, which is limited, especially under the heterogeneous setting, since it is intuitively "unfair" to enforce agents with high-quality data to achieve similar accuracy to those who contribute low-quality data, which may discourage the agents from participating in FL. In this work, we propose a formal FL fairness definition, fairness via agent-awareness (FAA), which takes different contributions of heterogeneous agents into account. Under FAA, the performance of agents with high-quality data will not be sacrificed just due to the existence of large amounts of agents with low-quality data. In addition, we propose a fair FL training algorithm based on agent clustering (FOCUS) to achieve fairness in FL measured by FAA. Theoretically, we prove the convergence and optimality of FOCUS under mild conditions for linear and general convex loss functions with bounded smoothness. We also prove that FOCUS always achieves higher fairness in terms of FAA compared with standard FedAvg under both linear and general convex loss functions. Empirically, we show that on four FL datasets, including synthetic data, images, and texts, FOCUS achieves significantly higher fairness in terms of FAA while maintaining competitive prediction accuracy compared with FedAvg and state-of-the-art fair FL algorithms.

翻译：摘要：联邦学习允许智能体在不共享本地数据的情况下共同训练全局模型。然而，由于本地数据的异构性，优化甚至定义训练所得全局模型对智能体的公平性颇具挑战。例如，现有工作通常将准确率公平性视为联邦学习中对不同智能体的公平性准则，这在高异构场景下存在局限，因为强制高质量数据的智能体与贡献低质量数据的智能体达到相似准确率，在直觉上"不公平"，甚至可能抑制智能体参与联邦学习的积极性。本文提出一种形式化的联邦学习公平性定义——智能体感知公平性，该定义充分考虑了异构智能体的不同贡献。在智能体感知公平性框架下，高质量数据智能体的性能不会因大量低质量数据智能体的存在而牺牲。此外，我们提出基于智能体聚类的公平联邦学习训练算法以实现智能体感知公平性衡量的联邦学习公平性。理论上，我们证明在线性损失函数和具有有界光滑性的广义凸损失函数的温和条件下，算法的收敛性与最优性，同时证明在两种损失函数下，该算法相比标准FedAvg总能实现更高的智能体感知公平性。实验表明，在包含合成数据、图像和文本的四个联邦学习数据集上，该算法在保持与FedAvg及最先进公平联邦学习算法相当预测准确率的同时，显著提升了智能体感知公平性。