mlsanityData to 7 Oct 2026

Multimodal Federated Learning in Healthcare: a Review

arXiv 2310.09650 ↗First posted 14 Oct 2023cs.LG cs.AIcited by 13

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Abstract (arXiv)

Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Simultaneously, Federated Learning (FL) has progressed, providing a decentralized mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. The integration of these two concepts supports the ongoing progress of multimodal learning in healthcare while ensuring the security and privacy of patient records within local data-holding agencies. This paper offers a concise overview of the significance of FL in healthcare and outlines the current state-of-the-art approaches to Multimodal Federated Learning (MMFL) within the healthcare domain. It comprehensively examines the existing challenges in the field, shedding light on the limitations of present models. Finally, the paper outlines potential directions for future advancements in the field, aiming to bridge the gap between cutting-edge AI technology and the imperative need for patient data privacy in healthcare applications.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 45 of its 103 references that are in the map. First posted on arXiv 14 Oct 2023.

Builds on

45 of 103 references.

References · 45

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  5. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  6. 2022Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learningcited by 553
  7. 2020Adaptive Risk Minimization: Learning to Adapt to Domain Shiftcited by 183
  8. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  9. 2016On Deep Multi-View Representation Learning: Objectives and Optimizationcited by 162
  10. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  11. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  12. 2022FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extractioncited by 102
33 more
  1. 2023Personalized Federated Learning with Feature Alignment and Classifier Collaborationcited by 97
  2. 2023Multimodal Federated Learning via Contrastive Representation Ensemblecited by 85
  3. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  4. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  5. 2022Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learningcited by 52
  6. 2022A Survey on Heterogeneous Federated Learningcited by 42
  7. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  8. 2022MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcarecited by 36
  9. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  10. 2021Multimodal Federated Learning on IoT Datacited by 32
  11. 2021Personalized Federated Learning with Adaptive Batchnorm for Healthcarecited by 26
  12. 2021Personalized Federated Learning with Contextualized Generalizationcited by 23
  13. 2022One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesiscited by 22
  14. 2021Meta-HAR: Federated Representation Learning for Human Activity Recognitioncited by 21
  15. 2022FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesiscited by 21
  16. 2022Mitigating Data Heterogeneity in Federated Learning with Data Augmentationcited by 21
  17. 2022Industry-Scale Orchestrated Federated Learning for Drug Discoverycited by 19
  18. 2020Federated Survival Analysis with Discrete-Time Cox Modelscited by 18
  19. 2020Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcarecited by 18
  20. 2023FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learningcited by 17
  21. 2022FedStack: Personalized activity monitoring using stacked federated learningcited by 15
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  23. 2022FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentationcited by 12
  24. 2023Towards Interpretable Federated Learningcited by 11
  25. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  26. 2022Study of the performance and scalability of federated learning for medical imaging with intermittent clientscited by 9
  27. 2021Cross-Domain Federated Learning in Medical Imagingcited by 6
  28. 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
  29. 2023Balancing Privacy Protection and Interpretability in Federated Learningcited by 6
  30. 2023Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomicscited by 6
  31. 2023Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstructioncited by 5
  32. 2021RingFed: Reducing Communication Costs in Federated Learning on Non-IID Datacited by 4
  33. 2023Trustworthy Privacy-preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 with Blockchaincited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 6.

13 papers.

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  1. 2025Multimodal Federated Learning With Missing Modalities through Feature Imputation Networkcited by 8
  2. 2024CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalitiescited by 5
  3. 2025ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysiscited by 2
  4. 2025Federated Foundation Model for GI Endoscopy Imagescited by 2
  5. 2024Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reportscited by 0
  6. 2024Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and futurecited by 0
  7. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0
  8. 2026Federated learning for unpaired multimodal data through a homogeneous transformer modelcited by 0
  9. 2026Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalizationcited by 0
  10. 2026FedVG: Gradient-Guided Aggregation for Enhanced Federated Learningcited by 0
  11. 2026ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalitiescited by 0
  12. 2026FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learningcited by 0
1 more
  1. 2026Prototype-guided Bilateral Alignment Multimodal Federated Learningcited by 0