Abstract :

The Rapid Adoption Of Artificial Intelligence And Data-driven Analytics In Healthcare Has Significantly Increased The Need For Secure And Privacy-preserving Patient Data Sharing Mechanisms. Traditional Centralized Machine Learning Approaches Require Aggregating Sensitive Medical Data From Multiple Institutions, Creating Risks Of Data Breaches, Unauthorized Access, And Regulatory Non-compliance. To Address These Challenges, This Study Proposes A Blockchain-Enabled Federated Learning (BCFL) Framework For Secure Patient Data Sharing In Medical Diagnostics. The Proposed Approach Enables Collaborative Model Training Across Distributed Healthcare Institutions While Ensuring That Raw Patient Data Remain Locally Stored. Blockchain Technology Is Integrated To Provide Tamper-resistant Recordkeeping, Decentralized Trust Management, And Secure Verification Of Model Updates Through Smart Contracts. This Combination Enhances Transparency, Accountability, And Integrity In Federated Learning Environments While Protecting Patient Privacy. Experimental Evaluation Demonstrates That The Framework Supports Accurate Multidisease Medical Diagnosis While Maintaining Strong Security, Scalability, And Resilience Against Cyber Threats. The Proposed BCFL System Provides A Reliable And Privacy-preserving Solution For Next-generation Intelligent Healthcare Systems. Keywords: Blockchain, Federated Learning, Medical Diagnostics, Patient Data Privacy, Secure Data Sharing, Healthcare AI, Decentralized Learning, Smart Contracts.

Published:

04-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

10-16

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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