Federated Multi-Modal Learning for Smart Healthcare Systems
DOI:
https://doi.org/10.15662/IJRAI.2025.0806002Keywords:
Federated Learning, Multi-Modal Data, Smart Healthcare, Privacy Preservation, Diagnostic Accuracy, Personalized Treatment, Wearable Sensors, Medical Imaging, Electronic Health Records, Convolutional Neural Networks, Recurrent Neural Networks, Attention MechanismsAbstract
The integration of Federated Learning (FL) with multi-modal data sources has emerged as a promising approach to enhance the capabilities of smart healthcare systems. Traditional centralized machine learning models often face challenges related to data privacy, security, and the heterogeneity of healthcare data. FL addresses these issues by enabling collaborative model training across decentralized devices without the need to share raw data. When combined with multi-modal data—such as electronic health records (EHRs), medical imaging, and wearable sensor data—FL can provide more comprehensive and accurate healthcare insights.ResearchGateMDPI+1 This paper explores the application of federated multi-modal learning in smart healthcare systems, focusing on its potential to improve diagnostic accuracy, personalized treatment, and patient monitoring. We discuss various methodologies employed in this domain, including the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms to process and integrate diverse data modalities. Additionally, we examine the challenges associated with modality heterogeneity, data imbalance, and communication efficiency in federated settings. Through a comprehensive review of existing literature, we highlight the advantages of federated multi-modal learning, such as enhanced privacy preservation, scalability, and the ability to leverage data from multiple institutions. However, we also address the limitations, including the complexity of model aggregation, potential biases in data distribution, and the need for robust security measures. The findings suggest that federated multi-modal learning holds significant promise for advancing smart healthcare systems. Future research should focus on developing more efficient aggregation algorithms, addressing data heterogeneity, and ensuring the interpretability of models to facilitate their adoption in clinical settings.
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