As federated learning gains increasing importance in real-world applications due to its capacity for decentralized data training, addressing fairness concerns across demographic groups becomes critically important. However, most existing machine learning algorithms for ensuring fairness are designed for centralized data environments and generally require large-sample and distributional assumptions, underscoring the urgent need for fairness techniques adapted for decentralized and heterogeneous systems with finite-sample and distribution-free guarantees. To address this issue, this paper introduces FedFaiREE, a post-processing algorithm developed specifically for distribution-free fair learning in decentralized settings with small samples. Our approach accounts for unique challenges in decentralized environments, such as client heterogeneity, communication costs, and small sample sizes. We provide rigorous theoretical guarantees for both fairness and accuracy, and our experimental results further provide robust empirical validation for our proposed method.

翻译：随着联邦学习因其去中心化数据训练能力在现实应用中日益重要，解决跨人口群体的公平性问题变得至关重要。然而，现有大多数确保公平性的机器学习算法专为集中式数据环境设计，通常需要大样本和分布假设，这凸显了亟需开发适用于去中心化异构系统、具备有限样本和无分布假设保证的公平性技术。针对这一问题，本文提出了FedFaiREE——一种专为去中心化环境下小样本无分布公平学习设计的后处理算法。我们的方法考虑了去中心化环境中的独特挑战，如客户端异质性、通信成本和小样本规模。我们为公平性和准确性提供了严格的理论保证，实验结果进一步为所提方法提供了稳健的实证验证。