用条件扩散模型显式填补多模态联邦学习中的缺失数据。
Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
- 用条件扩散模型根据已有模态信息重建缺失时序数据。
- 在三个临床数据集上达到与顶尖方法相当的性能。
- 适合处理传感器中断导致的数据缺失场景。
多模态联邦学习（MMFL）可实现隐私保护下的协作训练，但真实临床应用常因传感器间歇性或采样不规则导致模态内数据缺失。现有方法通过架构对齐或缺失嵌入隐式表示未观测数据，难以恢复真实分布，表现欠佳。本文提出 CondI，一种基于条件扩散模型的联邦框架，显式解决该问题。采用两阶段训练：首先利用可用多模态上下文与条件嵌入重建缺失时序成分；其次优化各模态提取器与联合嵌入空间。推理时，重建的原始数据经训练好的提取器生成鲁棒特征，为下游任务提供完整语义表示。显式数据填补使模型在严重数据不完整下仍保持强健性。在 PTB-XL、SLEEP-EDF、MIMIC-IV 三个临床数据集上的实验表明，CondI 性能媲美当前最优基线。代码已开源。
原文摘要 · Abstract (English)
Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-modality missingness caused by sensor intermittency or irregular sampling. Existing methods implicitly represent unobserved data via architectural alignment or missing embeddings, often failing to recover the true distribution and yielding sub-optimal performance. We propose CondI, a federated framework explicitly addressing this missingness using conditional diffusion models. CondI employs a two-phase training pipeline: first, imputing unobserved temporal components using available multimodal context and conditional embeddings; second, optimizing modality-specific extractors and joint embedding spaces. During inference, imputed raw data pass through trained extractors to generate robust features, providing a holistic representation for downstream tasks. Explicit data imputation ensures models operate on complete semantic structures, significantly enhancing resilience against severe data incompleteness. Experiments on three clinical datasets (PTB-XL, SLEEP-EDF, MIMIC-IV) demonstrate CondI achieves comparable results to state-of-the-art baselines. Code: https://github.com/ZhengWugeng/CondI
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