Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many practical settings, including healthcare, finance, and mobile sensing, the calibration data required for CP are distributed across multiple clients, each with its own local data distribution. In this federated setting, data can often be partitioned into, potentially overlapping, groups, which may reflect client-specific strata or cross-cutting attributes such as demographic or semantic categories. We propose group-conditional federated conformal prediction (GC-FCP), a novel protocol that provides group-conditional coverage guarantees. GC-FCP constructs mergeable, group-stratified coresets from local calibration scores, enabling clients to communicate compact weighted summaries that support efficient aggregation and calibration at the server. Experiments on synthetic and real-world datasets validate the performance of GC-FCP compared to centralized calibration baselines.

翻译：部署可信赖的人工智能系统需要基于原则的不确定性量化。共形预测（CP）是一种广泛使用的框架，用于构建具有无分布覆盖保证的预测集。在许多实际场景中，包括医疗保健、金融和移动感知，CP所需的校准数据分布在多个客户端之间，每个客户端拥有其自身的本地数据分布。在这种联邦设置中，数据通常可以被划分为（可能重叠的）组，这些组可能反映客户端特定的层次结构或交叉属性，例如人口统计或语义类别。我们提出了组条件联邦共形预测（GC-FCP），这是一种新颖的协议，提供组条件覆盖保证。GC-FCP从本地校准分数构建可合并的、组分层的核心集，使客户端能够通信紧凑的加权摘要，支持服务器端的高效聚合和校准。在合成和真实世界数据集上的实验验证了GC-FCP相较于集中式校准基线的性能。