解决多智能体中不确定性量化难题，实现个性化且隐私安全的置信预测。
Multi-Agent Conformal Prediction with Personalized Statistical Validity
- 通过局部密度比加权与加权分位数聚合，应对数据异构性挑战。
- 每个智能体均实现渐近有效的覆盖率，且通信仅需一次。
- 适用于医疗、金融等需隐私保护与个性化预测的场景。
不确定性量化在高风险机器学习任务中至关重要。然而，一种原理严谨的解决方案——共形预测，在本地校准数据有限、隐私约束和数据异构性条件下面临挑战。在多智能体设置中，现有方法无法同时妥善解决上述问题，且覆盖率保证仅限于平均值或在异构场景中失效。为此，我们提出个性化联邦加权共形预测（PFWCP）框架，结合局部密度比加权与加权分位数聚合，以纠正异构性并保持隐私。该方法为每个参与智能体提供渐近有效的边际覆盖率与校准条件覆盖率保证，并支持单次通信协议。理论分析揭示了覆盖方差的调整项，其由有效样本量表达式决定，这在加权共形预测中至关重要。在合成与真实数据集上的实验表明，其校准质量优于当前最先进的联邦共形基线。
原文摘要 · Abstract (English)
Uncertainty quantification is essential in high-stakes machine learning tasks. However, one of the principled solutions, conformal prediction, faces challenges under limited local calibration data, privacy constraints, and data heterogeneity. In multi-agent settings, existing works do not simultaneously and satisfactorily address these challenges with guarantees either limited to averages across agents or losing validity in heterogeneous settings. Hence, we propose personalized federated weighted conformal prediction (PFWCP), a framework that combines local density ratio weighting with weighted quantile aggregation to correct for heterogeneity while preserving privacy. The method yields asymptotically valid marginal and calibration-conditional coverage guarantees for each participating agent and supports protocols with one-shot communication. Theoretical analysis presents an adjustment to the coverage variance, governed by an effective sample size expression, which is necessary in the context of weighted conformal prediction, and experiments on synthetic and real datasets show improved calibration quality over state-of-the-art federated conformal baselines.
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