解决医疗联邦学习中模态缺失问题，通过补全表示空间提升模型鲁棒性。
FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning
- 在联邦学习中利用配对样本学习模态缺失时的表示修正，不生成图像数据。
- 隐藏心电图导致性能下降0.085 AUROC，补全后恢复49.0%的损失。
- 仅在有配对证据时启用补全，适合需验证支持的临床部署场景。
联邦多模态模型通常假设各医疗机构均具备所有模态数据，但实际中医院在电子病历（EHR）、胸片（CXR）和心电图（ECG）获取上存在差异。本文基于MIMIC数据集上的呼吸衰竭预测任务，构建模拟联邦学习客户端，提出FedCoRe（联邦跨模态表征补全）框架。该方法学习在表示空间或逻辑输出空间进行修正，而非生成合成的ECG或CXR图像。当客户端在训练时观察到某模态可能在部署时缺失，会使用同一实例有无该模态的配对样本获得监督信号。仅拥有此类配对样本的客户端更新补全过程，验证阶段可保留原始预测。评估时冻结多模态预测器，确保性能差异仅来自补全过程。隐藏ECG使AUROC下降约0.085；配对样本联邦平均（paired-example FedAvg）恢复0.0415 AUROC，即恢复了49.0%的性能损失。结果揭示两种效应：配对样本联邦平均部分弥补了缺失ECG带来的差距；而验证选择的补全实为任务特异性分类器逻辑修正，非真实ECG重建。对于CXR，在受控实验中，感知效应的补全恢复了52.8%的性能损失。配对样本联邦平均传递部分效果，但对输入缺乏CXR的部署案例，验证阶段仍保留无补全基线。因此，FedCoRe应被理解为一种验证门控的补全/修正框架：可在支持条件下恢复缺失模态信号，但仅当配对样本与验证证据支持时才可部署。
原文摘要 · Abstract (English)
Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and ECGs. We study this setting on a MIMIC-derived respiratory deterioration task with simulated FL clients and introduce FedCoRe (Federated Cross-Modal Representation Completion). FedCoRe learns representation- or logit-space corrections rather than generating synthetic ECGs or CXR images. When a client observes a modality that may be missing at deployment, it evaluates the same example with and without that modality to obtain paired supervision. Only clients with such pairs update the completion module, and validation may retain the unchanged prediction. We freeze the trained multimodal predictor during evaluation so that measured differences come only from completion. Hiding ECG reduced AUROC by about 0.085; paired-example FedAvg restored 0.0415 AUROC, or 49.0% of the lost performance. We therefore report two distinct effects: paired-example FedAvg partially recovers the missing-ECG gap, while validation-selected completion is a task-specific classifier-logit correction rather than literal ECG recovery. For CXR, effect-aware completion recovers 52.8% of the loss in a controlled test where CXR is hidden. Paired-example FedAvg transfers part of this effect, but validation keeps the no-completion baseline for deployment cases whose inputs lack CXR. Thus, FedCoRe should be read as a validation-gated completion/correction framework: it can recover missing-modality signal in supported settings, but it should be deployed only when paired examples and validation evidence support that modality.
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