Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this method outperforms current competitors.

翻译：联邦学习（FL）是一种机器学习框架，允许多个客户端在保持训练数据分散的同时协作训练模型。尽管联邦学习近年来取得了进展，但不确定性量化（UQ）问题仍未得到充分解决。在不确定性量化方法中，共形预测（CP）方法能在最少的假设条件下提供无分布保证。我们提出了一种基于分位数回归的新型联邦共形预测方法，并考虑了隐私约束。该方法利用重要性加权有效处理各智能体之间的标签偏移，并为预测集的有效覆盖率和差分隐私提供理论保证。大量实验研究表明，该方法优于现有竞争对手。