将公平性约束引入联邦学习中的置信预测，提升模型对不同群体的公平性保障。
FedCF: Fair Federated Conformal Prediction
- 在联邦学习中扩展公平置信预测框架，确保各群体覆盖率均衡。
- 通过分析不同群体的公平性差距，实现对联邦模型的公平性审计。
- 适用于关注模型公平性的医疗、金融等敏感领域研究者。
置信预测（Conformal Prediction, CP）是一种广泛用于量化机器学习模型不确定性的重要技术。标准形式的CP能提供真标签覆盖率的概率保证，但对数据集中的敏感属性不敏感。近期一些工作尝试将公平性融入CP，以确保不同子群体间具备条件覆盖率保证。其中一种方法是置信公平性（Conformal Fairness, CF）。本文将CF框架拓展至联邦学习场景，探讨如何通过分析不同人口统计群体的公平性相关差距，来审计联邦模型的公平性。我们在多个跨领域的数据集上进行了实验，充分依赖可交换性假设，验证了该框架的有效性。
原文摘要 · Abstract (English)
Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models. In its standard form, CP offers probabilistic guarantees on the coverage of the true label, but it is agnostic to sensitive attributes in the dataset. Several recent works have sought to incorporate fairness into CP by ensuring conditional coverage guarantees across different subgroups. One such method is Conformal Fairness (CF). In this work, we extend the CF framework to the Federated Learning setting and discuss how we can audit a federated model for fairness by analyzing the fairness-related gaps for different demographic groups. We empirically validate our framework by conducting experiments on several datasets spanning multiple domains, fully leveraging the exchangeability assumption.
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