Secure Multi-Modal Healthcare Analytics Framework Using Federated Learning and Blockchain Integration

Abstract

The integration of diverse healthcare data sources has significantly improved disease prediction and clinical decision-making; however, concerns regarding data privacy and security remain critical challenges. This paper presents a Secure Multi-Modal Healthcare Analytics Framework using federated learning and blockchain integration. The proposed system enables collaborative model training across multiple healthcare institutions without sharing sensitive patient data by utilizing federated learning. Multi-modal data, including medical images, electronic health records, and physiological signals, are processed using deep learning architectures to enhance predictive performance. Blockchain technology is incorporated to ensure transparency, immutability, and secure management of model updates through smart contracts. The framework also integrates differential privacy mechanisms to further safeguard patient information. Adaptive aggregation techniques are employed to improve model convergence and robustness across heterogeneous data sources. Experimental results demonstrate that the proposed approach achieves high predictive accuracy while maintaining strict data privacy and system security. Additionally, integration with prior research in healthcare analytics enhances scalability and generalization. The study highlights the potential of combining federated learning and blockchain technologies to develop secure, decentralized, and efficient healthcare analytics systems.