# Multimodal Federated Learning in Healthcare: a Review

arXiv: https://arxiv.org/abs/2310.09650 · HTML: https://mlsanity.com/paper/2310.09650/

## Key facts

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

## Builds on (most cited first, 30 of 45)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning](https://mlsanity.com/paper/2203.02053/) (2022)
- [Adaptive Risk Minimization: Learning to Adapt to Domain Shift](https://mlsanity.com/paper/2007.02931/) (2020)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2111.14213/) (2021)
- [FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction](https://mlsanity.com/paper/2212.01548/) (2022)
- [Personalized Federated Learning with Feature Alignment and Classifier Collaboration](https://mlsanity.com/paper/2306.11867/) (2023)
- [Multimodal Federated Learning via Contrastive Representation Ensemble](https://mlsanity.com/paper/2302.08888/) (2023)
- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge](https://mlsanity.com/paper/2101.07511/) (2021)
- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Communication Efficiency in Federated Learning: Achievements and Challenges](https://mlsanity.com/paper/2107.10996/) (2021)
- [MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare](https://mlsanity.com/paper/2206.08516/) (2022)
- [Specificity-Preserving Federated Learning for MR Image Reconstruction](https://mlsanity.com/paper/2112.05752/) (2021)
- [Multimodal Federated Learning on IoT Data](https://mlsanity.com/paper/2109.04833/) (2021)
- [Personalized Federated Learning with Adaptive Batchnorm for Healthcare](https://mlsanity.com/paper/2112.00734/) (2021)
- [Personalized Federated Learning with Contextualized Generalization](https://mlsanity.com/paper/2106.13044/) (2021)
- [One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis](https://mlsanity.com/paper/2207.06509/) (2022)
- [Meta-HAR: Federated Representation Learning for Human Activity Recognition](https://mlsanity.com/paper/2106.00615/) (2021)
- [FedMed-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis](https://mlsanity.com/paper/2201.08953/) (2022)
- [Mitigating Data Heterogeneity in Federated Learning with Data Augmentation](https://mlsanity.com/paper/2206.09979/) (2022)
- [Federated Survival Analysis with Discrete-Time Cox Models](https://mlsanity.com/paper/2006.08997/) (2020)
- [Industry-Scale Orchestrated Federated Learning for Drug Discovery](https://mlsanity.com/paper/2210.08871/) (2022)

## Built on by (most cited first, 13 of 13)

- [Multimodal Federated Learning With Missing Modalities through Feature Imputation Network](https://mlsanity.com/paper/2505.20232/) (2025)
- [CAR-MFL: Cross-Modal Augmentation by Retrieval for Multimodal Federated Learning with Missing Modalities](https://mlsanity.com/paper/2407.08648/) (2024)
- [ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis](https://mlsanity.com/paper/2502.12180/) (2025)
- [Federated Foundation Model for GI Endoscopy Images](https://mlsanity.com/paper/2505.24108/) (2025)
- [Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports](https://mlsanity.com/paper/2407.09064/) (2024)
- [Generative AI like ChatGPT in Blockchain Federated Learning: use cases, opportunities and future](https://mlsanity.com/paper/2407.18358/) (2024)
- [Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms](https://mlsanity.com/paper/2505.21792/) (2025)
- [Federated learning for unpaired multimodal data through a homogeneous transformer model](https://mlsanity.com/paper/2601.17986/) (2026)
- [Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalization](https://mlsanity.com/paper/2602.20773/) (2026)
- [FedVG: Gradient-Guided Aggregation for Enhanced Federated Learning](https://mlsanity.com/paper/2602.21399/) (2026)
- [ProMoE-FL: Prototype-conditioned Mixture of Experts for Multimodal Federated Learning with Missing Modalities](https://mlsanity.com/paper/2607.06633/) (2026)
- [FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning](https://mlsanity.com/paper/2608.18311/) (2026)
- [Prototype-guided Bilateral Alignment Multimodal Federated Learning](https://mlsanity.com/paper/2609.38925/) (2026)

## 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.