Multimodal Federated Learning in Healthcare: A Review

Area of Science:

• Artificial Intelligence
• Machine Learning
• Healthcare Informatics

Background:

• Multimodal machine learning advances AI in medicine, often using centralized data.
• Federated Learning (FL) offers a decentralized alternative, enhancing data privacy and security.
• Integrating FL with multimodal learning addresses healthcare data privacy concerns.

Purpose of the Study:

• To provide an overview of Federated Learning's significance in healthcare.
• To outline current state-of-the-art Multimodal Federated Learning (MMFL) approaches in medicine.
• To examine challenges and future directions in MMFL for healthcare.

Main Methods:

• Literature review of current Multimodal Federated Learning (MMFL) techniques.
• Analysis of existing challenges and limitations in MMFL models for healthcare.
• Exploration of future research avenues for MMFL in the medical domain.

Main Results:

• MMFL leverages decentralized data for secure, privacy-preserving AI in healthcare.
• Current MMFL models face challenges in implementation and scalability.
• Significant potential exists for advancing MMFL to bridge AI capabilities with data privacy.

Conclusions:

• Multimodal Federated Learning is crucial for secure and private AI in healthcare.
• Addressing current challenges is key to unlocking the full potential of MMFL.
• Future research should focus on robust MMFL frameworks for widespread healthcare adoption.