Multimodal Federated Learning in Healthcare: a Review

TL;DR

This review discusses how multimodal federated learning enhances healthcare AI by combining multiple data types while preserving patient privacy, highlighting current approaches, challenges, and future research directions.

Contribution

It provides a comprehensive overview of the state-of-the-art in multimodal federated learning for healthcare and identifies key challenges and future opportunities.

Findings

Current models face significant privacy and data heterogeneity challenges.

Federated learning enables decentralized training without sharing raw patient data.

Future research should focus on improving model robustness and addressing data privacy concerns.

Abstract

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.