Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a central server without sharing their raw training data. While FL offers notable advantages, it faces critical challenges in ensuring fairness across diverse demographic groups. To address these fairness concerns, various fairness-aware debiasing methods have been proposed. However, many of these approaches either require modifications to clients' training protocols or lack flexibility in their aggregation strategies. In this work, we address these limitations by introducing EquFL, a novel server-side debiasing method designed to mitigate bias in FL systems. EquFL operates by allowing the server to generate a single calibrated update after receiving model updates from the clients. This calibrated update is then integrated with the aggregated client updates to produce an adjusted global model that reduces bias. Theoretically, we establish that EquFL converges to the optimal global model achieved by FedAvg and effectively reduces fairness loss over training rounds. Empirically, we demonstrate that EquFL significantly mitigates bias within the system, showcasing its practical effectiveness.

翻译：联邦学习（FL）已成为一种变革性的分布式学习范式，允许多个客户端在中央服务器的协调下协同训练全局模型，同时无需共享其原始训练数据。尽管FL具有显著优势，但在确保不同人口群体的公平性方面面临严峻挑战。为解决这些公平性问题，研究者提出了多种公平感知的去偏方法。然而，其中许多方法要么要求修改客户端的训练协议，要么在聚合策略上缺乏灵活性。在本工作中，我们通过引入EquFL——一种新颖的服务器端去偏方法——来克服这些局限，旨在缓解FL系统中的偏差。EquFL的工作原理是：服务器在接收到客户端的模型更新后，生成单个校准更新。此校准更新随后与聚合后的客户端更新相结合，生成能降低偏差的调整后全局模型。理论上，我们证明了EquFL能收敛至通过FedAvg得到的最优全局模型，并在训练轮次中有效减少公平性损失。实验表明，EquFL显著减轻了系统内的偏差，展示了其实际有效性。