在设备数据少且通信受限时，用消息传递实现可靠预测集
Distributed Conformal Prediction via Message Passing
- 通过消息传递在分布式设备间协作计算预测集
- 两种方法均保证统计覆盖率，且在不同网络下表现稳定
- 适合医疗等安全敏感场景的模型可靠性校准
事后校准预训练模型对确保安全关键领域（如医疗）中的可靠推断至关重要。共形预测（Conformal Prediction, CP）提供了一种鲁棒的事后校准框架，通过利用保留数据集为预测集提供分布无关的统计覆盖率保证。本文针对一种去中心化设置：每个设备仅有少量校准数据，且只能与邻居节点通过任意图拓扑通信。我们提出两种基于消息传递的分布式共形预测方法：基于分位数的分布式共形预测（Q-DCP）和基于直方图的分布式共形预测（H-DCP）。Q-DCP采用增强平滑与正则化的分布式分位数回归以加速收敛，H-DCP则使用基于共识的直方图估计方法。通过大量实验，我们研究了不同网络拓扑下超参数调优需求、通信开销、覆盖率保证与预测集大小之间的权衡。代码已开源：https://github.com/HaifengWen/Distributed-Conformal-Prediction。
原文摘要 · Abstract (English)
Post-hoc calibration of pre-trained models is critical for ensuring reliable inference, especially in safety-critical domains such as healthcare. Conformal Prediction (CP) offers a robust post-hoc calibration framework, providing distribution-free statistical coverage guarantees for prediction sets by leveraging held-out datasets. In this work, we address a decentralized setting where each device has limited calibration data and can communicate only with its neighbors over an arbitrary graph topology. We propose two message-passing-based approaches for achieving reliable inference via CP: quantile-based distributed conformal prediction (Q-DCP) and histogram-based distributed conformal prediction (H-DCP). Q-DCP employs distributed quantile regression enhanced with tailored smoothing and regularization terms to accelerate convergence, while H-DCP uses a consensus-based histogram estimation approach. Through extensive experiments, we investigate the trade-offs between hyperparameter tuning requirements, communication overhead, coverage guarantees, and prediction set sizes across different network topologies. The code of our work is released on: https://github.com/HaifengWen/Distributed-Conformal-Prediction.
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