提出兼顾全局与局部公平的联邦学习框架，量化公平代价。
The Cost of Local and Global Fairness in Federated Learning
- 基于贝叶斯最优得分函数设计后处理算法
- 多分类场景下实现更优的准确率-公平性权衡
- 降低计算与通信开销，适合金融医疗等场景
随着联邦学习在金融、招聘和医疗等领域的应用，模型需满足公平性要求，避免对种族、性别等受保护属性产生偏差。联邦学习中的公平性分为全局公平（整体人群差异）与局部公平（各客户端内部差异）。现有方法通常只关注其中一种，且多限于二分类问题。本文提出一个框架，用于分析在多分类联邦学习中实现指定水平全局与局部公平时的最小准确率损失。该框架导出一种简单后处理算法，可从贝叶斯最优得分函数生成公平预测器。实验表明，该算法在准确率-公平性权衡、计算与通信成本方面均优于当前最先进方法。代码已开源。
原文摘要 · Abstract (English)
With the emerging application of Federated Learning (FL) in finance, hiring and healthcare, FL models are regulated to be fair, preventing disparities with respect to legally protected attributes such as race or gender. Two concepts of fairness are important in FL: global and local fairness. Global fairness addresses the disparity across the entire population and local fairness is concerned with the disparity within each client. Prior fair FL frameworks have improved either global or local fairness without considering both. Furthermore, while the majority of studies on fair FL focuses on binary settings, many real-world applications are multi-class problems. This paper proposes a framework that investigates the minimum accuracy lost for enforcing a specified level of global and local fairness in multi-class FL settings. Our framework leads to a simple post-processing algorithm that derives fair outcome predictors from the Bayesian optimal score functions. Experimental results show that our algorithm outperforms the current state of the art (SOTA) with regard to the accuracy-fairness tradoffs, computational and communication costs. Codes are available at: https://github.com/papersubmission678/The-cost-of-local-and-global-fairness-in-FL .
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