Protecting patient privacy remains a fundamental barrier to scaling machine learning across healthcare institutions, where centralizing sensitive data is often infeasible due to ethical, legal, and regulatory constraints. Federated learning offers a promising alternative by enabling privacy-preserving, multi-institutional training without sharing raw patient data; however, real-world deployments face severe challenges from data heterogeneity, site-specific biases, and class imbalance, which degrade predictive reliability and render existing uncertainty quantification methods ineffective. Here, we present TrustFed, a federated uncertainty quantification framework that provides distribution-free, finite-sample coverage guarantees under heterogeneous and imbalanced healthcare data, without requiring centralized access. TrustFed introduces a representation-aware client assignment mechanism that leverages internal model representations to enable effective calibration across institutions, along with a soft-nearest threshold aggregation strategy that mitigates assignment uncertainty while producing compact and reliable prediction sets. Using over 430,000 medical images across six clinically distinct imaging modalities, we conduct one of the most comprehensive evaluations of uncertainty-aware federated learning in medical imaging, demonstrating robust coverage guarantees across datasets with diverse class cardinalities and imbalance regimes. By validating TrustFed at this scale and breadth, our study advances uncertainty-aware federated learning from proof-of-concept toward clinically meaningful, modality-agnostic deployment, positioning statistically guaranteed uncertainty as a core requirement for next-generation healthcare AI systems.

翻译：保护患者隐私仍是跨医疗机构扩展机器学习的根本障碍，由于伦理、法律和监管限制，集中化敏感数据往往不可行。联邦学习提供了一种有前景的替代方案，能够在无需共享原始患者数据的情况下实现隐私保护的多机构联合训练；然而，实际部署面临着数据异质性、机构特有偏差和类别不平衡等严峻挑战，这些因素会降低预测可靠性，并使现有不确定性量化方法失效。本文提出TrustFed——一种联邦不确定性量化框架，能在异质性和不平衡的医疗数据下提供无需分布假设、有限样本覆盖保证，且无需集中式数据访问。TrustFed引入了表征感知的客户端分配机制，利用模型内部表征实现跨机构的有效校准，并采用软最近邻阈值聚合策略，在缓解分配不确定性的同时生成紧凑且可靠的预测集。我们利用来自六种临床不同成像模态的超过43万张医学图像，进行了医学影像领域不确定性感知联邦学习最全面的评估之一，证明了该框架在不同类别数量和失衡程度的多样化数据集上均能提供稳健的覆盖保证。通过在此规模和广度上验证TrustFed，本研究将不确定性感知联邦学习从概念验证推进到临床有意义的、模态无关的部署，将统计保证的不确定性定位为下一代医疗AI系统的核心要求。