Federated Learning (FL) allows collaborative model training among distributed parties without pooling local datasets at a central server. However, the distributed nature of FL poses challenges in training fair federated learning models. The existing techniques are often limited in offering fairness flexibility to clients and performance. We formally define and empirically analyze a simple and intuitive post-processing-based framework to improve group fairness in FL systems. This framework can be divided into two stages: a standard FL training stage followed by a completely decentralized local debiasing stage. In the first stage, a global model is trained without fairness constraints using a standard federated learning algorithm (e.g. FedAvg). In the second stage, each client applies fairness post-processing on the global model using their respective local dataset. This allows for customized fairness improvements based on clients' desired and context-guided fairness requirements. We demonstrate two well-established post-processing techniques in this framework: model output post-processing and final layer fine-tuning. We evaluate the framework against three common baselines on four different datasets, including tabular, signal, and image data, each with varying levels of data heterogeneity across clients. Our work shows that this framework not only simplifies fairness implementation in FL but also provides significant fairness improvements with minimal accuracy loss or even accuracy gain, across data modalities and machine learning methods, being especially effective in more heterogeneous settings.

翻译：联邦学习（Federated Learning, FL）允许多个分布式参与方协同训练模型，而无需将本地数据集汇集到中央服务器。然而，联邦学习的分布式特性给训练公平的联邦学习模型带来了挑战。现有技术通常在向客户端提供公平性灵活性和性能方面存在局限。本文正式定义并实证分析了一个简单直观的、基于后处理的框架，以提升联邦学习系统中的群体公平性。该框架可分为两个阶段：一个标准的联邦学习训练阶段，随后是一个完全去中心化的局部去偏阶段。在第一阶段，使用标准的联邦学习算法（例如FedAvg）训练一个无公平性约束的全局模型。在第二阶段，每个客户端利用其各自的本地数据集，对该全局模型进行公平性后处理。这使得客户端能够根据其期望的、由上下文引导的公平性要求，实现定制化的公平性提升。我们在该框架中展示了两种成熟的后处理技术：模型输出后处理与最终层微调。我们在四个不同的数据集（包括表格数据、信号数据和图像数据）上，针对三种常见基线方法对该框架进行了评估，每个数据集在客户端间具有不同程度的数据异质性。我们的研究表明，该框架不仅简化了联邦学习中公平性的实现，而且能在不同数据模态和机器学习方法上，以最小的精度损失甚至精度提升为代价，带来显著的公平性改进，在数据异质性更高的环境中尤为有效。