Blockchain-Integrated Federated Learning Framework for Secure and Decentralized Healthcare Intelligence
DOI:
https://doi.org/10.5281/ijurd.v2i2.32Keywords:
IoT Healthcare, Remote Monitoring, Edge Computing, Real-Time Processing, Wearable DevicesAbstract
The need for secure, privacy-preserving, and collaborative healthcare analytics has driven the integration of advanced technologies such as blockchain and federated learning. This paper presents a Blockchain-Integrated Federated Learning Framework for Secure and Decentralized Healthcare Intelligence. The proposed system enables multiple healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data, using federated learning principles. Blockchain technology is incorporated to ensure transparency, immutability, and trust in model updates and data transactions. Smart contracts are utilized to manage access control, validate model contributions, and enforce data-sharing policies. The framework addresses challenges related to data privacy, security, and trust while maintaining high predictive performance. Additionally, differential privacy and secure aggregation techniques are integrated to further enhance data protection. Experimental results demonstrate that the proposed approach achieves comparable accuracy to centralized models while ensuring data confidentiality and system integrity. Integration with prior research in healthcare analytics strengthens robustness and scalability. The study highlights the potential of combining blockchain and federated learning to develop secure, decentralized, and intelligent healthcare systems for next-generation medical applications.
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Copyright (c) 2026 Arun Roy, Sandeep Brar, Pradeep Khanna
This work is licensed under a Creative Commons Attribution 4.0 International License.
