Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge arises from modality heterogeneity: many clinical sites possess only a subset of modalities due to resource constraints or workflow variations. Existing approaches address this through feature imputation networks that synthesize missing modality representations, yet these methods produce point estimates without reliability measures, forcing downstream classifiers to treat all imputed features as equally trustworthy. In safety-critical medical applications, this limitation poses significant risks. We propose the Probabilistic Feature Imputation Network (P-FIN), which outputs calibrated uncertainty estimates alongside imputed features. This uncertainty is leveraged at two levels: (1) locally, through sigmoid gating that attenuates unreliable feature dimensions before classification, and (2) globally, through Fed-UQ-Avg, an aggregation strategy that prioritizes updates from clients with reliable imputation. Experiments on federated chest X-ray classification using CheXpert, NIH Open-I, and PadChest demonstrate consistent improvements over deterministic baselines, with +5.36% AUC gain in the most challenging configuration.

翻译：多模态联邦学习能够在医疗保健机构间实现隐私保护的协作模型训练。然而，一个根本性挑战源于模态异质性：许多临床站点因资源限制或工作流程差异仅拥有部分模态数据。现有方法通过特征补全网络来合成缺失的模态表征，但这些方法仅生成点估计而不提供可靠性度量，迫使下游分类器将所有补全特征视为同等可信。在安全关键的医疗应用中，这一局限带来了显著风险。我们提出概率化特征补全网络（P-FIN），该网络能够在输出补全特征的同时提供校准的不确定性估计。这种不确定性在两个层面被利用：（1）局部层面，通过sigmoid门控机制在分类前抑制不可靠的特征维度；（2）全局层面，通过Fed-UQ-Avg聚合策略，优先采用来自可靠补全客户端的更新。在基于CheXpert、NIH Open-I和PadChest数据集的联邦胸部X光分类实验中，相较于确定性基线方法，本方法在最具挑战性的配置下取得了+5.36% AUC的提升，展现出持续的性能改进。