FedCoRe is a federated learning framework that addresses missing modalities in healthcare by learning representation- or logit-space corrections instead of generating synthetic data. In a MIMIC-derived respiratory deterioration task, the method uses paired examples where a modality is present or absent to train a completion module, achieving partial recovery of performance lost when modalities like ECG or CXR are hidden. The approach emphasizes validation-gated deployment, ensuring that completion is only applied when paired examples and validation evidence support the presence of the missing modality.
By Holger R. Roth, Ziyue Xu, Peter Cnudde
arXiv:2608. 09240v1 Announce Type: cross Abstract: Multimodal federated learning (FL) supports collaborative modeling in privacy-sensitive health-sensing and medical settings, but realistic deployments often exhibit dual-axis modality missingness: clients have different modality sets, and individual samples may contain only subsets of the modalities available locally.
By Adiba Orzikulova, Jaehyun Kwak, Jaemin Shin, Yunqi Guo, Xiaomin Ouyang, Guoliang Xing, Steven Euijong Whang, Sung-Ju Lee
arXiv:2607. 06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality.
By Aavash Chhetri, Bibek Niroula, Eduard Vazquez, Yash Raj Shrestha, Prashnna Gyawali, Loris Bazzani, Binod Bhattarai
Fed-ReMasker is a federated learning approach that adapts the ReMasker masked autoencoder for tabular data imputation, specifically addressing feature-level missingness where entire features are absent at some centers. The method enables centers to impute unobserved features by leveraging knowledge from collaborating institutions. In benchmark tests on synthetic and real-world datasets, Fed-ReMasker achieves the lowest imputation error in the majority of scenarios and remains robust to client heterogeneity, closely matching the performance of a centralized model.
By Ioannis Papathanail, Rooholla Poursoleymani, Lubnaa Abdur Rahman, Stavroula Georgia Mougiakakou
arXiv:2608.21941v1 Announce Type: new
Abstract: Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal...
By Yixin Yang, Yueyang Sun, Weichen Liu, Xianbing Zhao, Sicen Liu
Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal electronic health records (EHRs), including struc...
arXiv:2607. 09982v1 Announce Type: new Abstract: Electronic health record (EHR) data are inherently multimodal, and leveraging multiple modalities can improve predictive performance.
By Nikkie Hooman, Zhongjie Wu, Eric C. Larson, Mehak Gupta
arXiv:2608. 13911v1 Announce Type: new Abstract: Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individual records within the same client may contain different partial modality subsets.
By Adiba Orzikulova, Dong Min Kim, Jaehong Yoon, Sung-Ju Lee
The paper introduces MUC-FL, a block‑wise marginal utility contribution framework that selectively transmits only the most impactful data blocks in federated learning to reduce communication overhead. Applied to a multimodal dataset derived from multiple MIMIC clinical datasets, the method identifies 24 out of 1,135 candidate blocks (1.76%) as carrying meaningful improvement signals, potentially cutting communication by 45‑50% while preserving or enhancing model quality. The deduplication‑based block selection achieves a macro F1 score of 0.8566 versus 0.8155 for standard federated optimization, showing improved performance especially for underrepresented classes.
By Akshay Mhatre, Vikram Karthick, Deepti Gupta, Jia Zou
arXiv:2607. 22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on structured event codes alone, and do not incorporate multiple modalities in a principled way.
By Yuxuan Liu, Joshua Placidi, Jinpei Han, Alfred John Balston, Marek Rei, A. Aldo Faisal
arXiv:2606. 12362v1 Announce Type: cross Abstract: We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made.
By Hui Wang, Tianyu Ren, Joseph Butler, Christopher Baker, Karen Rafferty, Simon McDade
arXiv:2607. 09884v1 Announce Type: cross Abstract: Multimodal medical models often degrade when inputs are missing, a common scenario in real-world clinical workflows.
By Nusrat Binta Nizam, Fengbei Liu, Sunwoo Kwak, Minh Nguyen, Ruining Deng, Mert R. Sabuncu