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（Conformal Prediction, CP）作为一种强大的不确定性量化框架，对于确保预测的可靠性至关重要。然而，常见的CP方法严重依赖于数据可交换性，这一条件在实践中常常被违背。现有的处理非可交换性的方法，往往导致其计算可行性仅限于最简单的示例。本文提出了一种新的高效CP方法，能够为相当广泛的非可交换数据分布生成可证明有效的置信集。我们通过将该通用理论应用于智能体间存在数据异质性的联邦学习这一挑战性场景来加以阐释。我们的方法允许以完全联邦化的方式，为智能体构建可证明有效的个性化预测集。在一系列真实世界数据集上的实验证明了所提方法的有效性。