mlsanityData to 14 Oct 2026

ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities

arXiv 2607.06633 ↗First posted 7 Jul 2026cs.CV cs.AIcited by 0

On the map: Mixture-of-experts → Medical and biomedical MoE

Abstract (arXiv)

In this paper, we address the problem of multimodal federated learning with missing modality. Existing methods utilize an additional public dataset or perform naive feature synthesis that is based solely on the available modality. To address these limitations, we propose ProMoE-FL, a Prototype-conditioned Mixture-of-Experts framework for robust missing-modality feature synthesis in multimodal federated learning. ProMoE-FL builds a global client-aware prototype bank that captures clinically meaningful modality priors across institutions. Our Mixture of Experts is conditioned on these prototypes and modality indices to enable direction-aware expert routing for dynamically synthesizing missing features. We perform extensive quantitative and qualitative evaluations on four public chest X-ray datasets (MIMIC-CXR, NIH Open-I, PadChest, and CheXpert) and demonstrate that ProMoE-FL consistently outperforms state-of-the-art methods in both homogeneous as well as the more challenging heterogeneous settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. Builds on 24 of its 35 references that are in the map. First posted on arXiv 7 Jul 2026.

Builds on

24 of 35 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  5. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2019PadChest: A large chest x-ray image dataset with multi-label annotated reportscited by 309
  8. 2021SMIL: Multimodal Learning with Severely Missing Modalitycited by 245
  9. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 163
  10. 2023Multimodal Federated Learning via Contrastive Representation Ensemblecited by 86
  11. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  12. 2023Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentationcited by 47
12 more
  1. 2022Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Trainingcited by 26
  2. 2023Medical Vision Language Pretraining: A surveycited by 24
  3. 2024Cross-Modal Prototype based Multimodal Federated Learning under Severely Missing Modalitycited by 21
  4. 2023Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrastcited by 17
  5. 2019A unified representation network for segmentation with missing modalitiescited by 15
  6. 2023Vision-Language Generative Model for View-Specific Chest X-ray Generationcited by 14
  7. 2023Multimodal Federated Learning in Healthcare: a Reviewcited by 13
  8. 2024Towards Multi-modal Transformers in Federated Learningcited by 12
  9. 2024Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detectioncited by 8
  10. 2025Multimodal Federated Learning With Missing Modalities through Feature Imputation Networkcited by 8
  11. 2024CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalitiescited by 5
  12. 2026Med-MMFL: A Multimodal Federated Learning Benchmark in Healthcarecited by 1

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  1. 2026Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
  2. 2026MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity
  3. 2024Flex-MoE: Modeling Arbitrary Modality Combination via the Flexible Mixture-of-Experts
  4. 2026FM2: Unified Federated Foundation Models for Heterogeneous Multimodal Medical Imaging
  5. 2026LongMoE: Longitudinal Multimodal Learning via Trajectory-Aware Mixture-of-Experts
  6. 2023ConDistFL: Conditional Distillation for Federated Learning from Partially Annotated Data
  7. 2025dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
  8. 2026Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts