Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification, which is crucial for ensuring the reliability of predictions. However, common CP methods heavily rely on data exchangeability, a condition often violated in practice. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions. We illustrate the general theory with applications to the challenging setting of federated learning under data heterogeneity between agents. Our method allows constructing provably valid personalized prediction sets for agents in a fully federated way. The effectiveness of the proposed method is demonstrated in a series of experiments on real-world datasets.

翻译：保形预测（CP）作为一种稳健的不确定性量化框架，对确保预测可靠性至关重要。然而，常见CP方法高度依赖数据可交换性，这一条件在实际中常被违反。现有处理非可交换性的方法仅适用于最简单的示例场景。本文提出一种新颖的高效CP方法，可为相当一般的非可交换数据分布生成可证明有效的置信集。我们通过代理间数据异质性的联邦学习这一挑战性场景来阐释该通用理论。我们的方法允许以完全联邦化的方式为各代理构建可证明有效的个性化预测集。在真实世界数据集上的一系列实验证明了所提方法的有效性。