联邦学习中实现按输入自适应的置信预测，提升医疗等高风险场景可靠性。
Federated Conditional Conformal Prediction via Generative Models
- 用生成模型（如扩散模型）建模各客户端条件分布，避免数据共享。
- 在真实数据集上验证，预测集能更好适应本地数据异质性。
- 适合对不确定性敏感的医疗、金融等联邦学习应用。
置信预测（Conformal Prediction, CP）通过构建保证覆盖真实标签的预测集，提供无需分布假设的不确定性量化，对多中心医疗等高风险联邦学习场景尤为重要。然而，标准CP假设数据独立同分布，这在联邦设置下不成立，因客户端间分布差异显著。现有联邦CP方法虽确保各客户端边际覆盖，但难以反映输入相关的不确定性。本文提出基于生成模型的联邦条件置信预测（Fed-CCP），旨在实现随输入变化的条件覆盖，以适应本地数据异质性。Fed-CCP利用归一化流或扩散模型等生成模型，近似条件数据分布，无需共享原始数据。各客户端可本地校准反映其独特不确定性的置信分数，同时通过联邦聚合保持全局一致性。实验证明，该方法在真实数据集上实现了更自适应的预测集。
原文摘要 · Abstract (English)
Conformal Prediction (CP) provides distribution-free uncertainty quantification by constructing prediction sets that guarantee coverage of the true labels. This reliability makes CP valuable for high-stakes federated learning scenarios such as multi-center healthcare. However, standard CP assumes i.i.d. data, which is violated in federated settings where client distributions differ substantially. Existing federated CP methods address this by maintaining marginal coverage on each client, but such guarantees often fail to reflect input-conditional uncertainty. In this work, we propose Federated Conditional Conformal Prediction (Fed-CCP) via generative models, which aims for conditional coverage that adapts to local data heterogeneity. Fed-CCP leverages generative models, such as normalizing flows or diffusion models, to approximate conditional data distributions without requiring the sharing of raw data. This enables each client to locally calibrate conformal scores that reflect its unique uncertainty, while preserving global consistency through federated aggregation. Experiments on real datasets demonstrate that Fed-CCP achieves more adaptive prediction sets.
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