服务器生成校准更新，公平提升联邦学习效果
When the Server Steps In: Calibrated Updates for Fair Federated Learning
- 服务器在接收客户端更新后，生成单一校准更新以减少偏差
- 实验表明该方法显著降低系统偏差，且收敛到最优全局模型
- 无需修改客户端训练流程，适合实际部署的公平性优化
联邦学习（FL）作为一种分布式学习范式，允许多个客户端在中央服务器协调下协同训练全局模型，而无需共享原始数据。尽管具有显著优势，但其在不同人口群体间公平性保障方面仍面临挑战。现有公平性增强方法或需修改客户端训练流程，或聚合策略缺乏灵活性。本文提出EquFL，一种全新的服务器端去偏方法：服务器在接收客户端更新后，生成一个校准更新，并与聚合后的客户端更新结合，得到修正后的全局模型以降低偏差。理论上，我们证明EquFL可收敛至与FedAvg相同的最优全局模型，并有效减少训练过程中的公平性损失。实验证明，EquFL能显著缓解系统内偏差，具备良好的实用性。
原文摘要 · Abstract (English)
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.
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