Conformal prediction has shown impressive capacity in constructing
statistically rigorous prediction sets for machine learning models with
exchangeable data samples. The siloed datasets, coupled with the escalating
privacy concerns related to local data sharing, have inspired recent
innovations extending conformal prediction into federated environments with
distributed data samples. However, this framework for distributed uncertainty
quantification is susceptible to Byzantine failures. A minor subset of
malicious clients can significantly compromise the practicality of coverage
guarantees. To address this vulnerability, we introduce a novel framework
Rob-FCP, which executes robust federated conformal prediction, effectively
countering malicious clients capable of reporting arbitrary statistics with the
conformal calibration process. We theoretically provide the conformal coverage
bound of Rob-FCP in the Byzantine setting and show that the coverage of Rob-FCP
is asymptotically close to the desired coverage level. We also propose a
malicious client number estimator to tackle a more challenging setting where
the number of malicious clients is unknown to the defender and theoretically
shows its effectiveness. We empirically demonstrate the robustness of Rob-FCP
against diverse proportions of malicious clients under a variety of Byzantine
attacks on five standard benchmark and real-world healthcare datasets.

在分布式数据样本中，介绍了 Rob-FCP 框架用于对付具有恶意客户端的鲁棒联邦一致性预测，理论上证明了 Rob-FCP 的一致性覆盖界限，并展示了 Rob-FCP 在拜占庭场景下与期望覆盖水平渐近接近，同时提出了一个用于估计恶意客户端数量的方法，在多种拜占庭攻击下，在五个标准基准和实际医疗数据集上经验证明了 Rob-FCP 的鲁棒性。