标题： FeDa4Fair：用于公平性评估的客户端级联邦数据集 显示英文标题

标题： FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation

摘要： 联邦学习（FL）使得在不共享客户端私有数据的情况下，多个客户端可以协作进行模型训练。 然而，公平性仍然是一个关键问题，因为本地客户端数据集中的偏差可能会影响整个联邦系统。 客户端之间异构的数据分布可能导致某些客户端的模型比其他客户端更公平。 尽管文献中存在一些增强公平性的解决方案，但大多数专注于减轻单一敏感属性的偏差，通常是二元的，忽略了不同客户端的不同且有时冲突的公平需求。 这种有限的视角可能会限制对不同客户端的公平干预的有效性。 为了支持联邦学习中更稳健和可重复的公平性研究，我们旨在在全局和客户端层面实现公平意识的联邦学习方法的一致基准测试。 在本文中，我们从三个方面做出贡献： （1）我们介绍了FeDa4Fair，一个用于生成表格数据集的库，这些数据集针对在异构客户端偏差下评估公平联邦学习方法；（2）我们发布了四个具有偏差异构性的数据集以及相应的基准，以在受控环境中比较公平性缓解方法；（3）我们提供了可用于评估这些数据集公平性结果的即用型函数。 显示英文摘要

摘要： Federated Learning (FL) enables collaborative model training across multiple clients without sharing clients' private data. However, fairness remains a key concern, as biases in local clients' datasets can impact the entire federated system. Heterogeneous data distributions across clients may lead to models that are fairer for some clients than others. Although several fairness-enhancing solutions are present in the literature, most focus on mitigating bias for a single sensitive attribute, typically binary, overlooking the diverse and sometimes conflicting fairness needs of different clients. This limited perspective can limit the effectiveness of fairness interventions for the different clients. To support more robust and reproducible fairness research in FL, we aim to enable a consistent benchmarking of fairness-aware FL methods at both the global and client levels. In this paper, we contribute in three ways: (1) We introduce FeDa4Fair, a library to generate tabular datasets tailored to evaluating fair FL methods under heterogeneous client bias; (2) we release four bias-heterogeneous datasets and corresponding benchmarks to compare fairness mitigation methods in a controlled environment; (3) we provide ready-to-use functions for evaluating fairness outcomes for these datasets.