Vol. 3 No. 2 (2025): Issue - 02
Articles

Securing Patient Data With Blockchain Enabled Federated Learning For Medical Diagnostics

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  • S Nadiya,
  • S Afrin Taj,
  • Y Jaswanth Reddy,
  • V Siva Praneeth Reddy,
  • V C Sai Vignesh,
  • S Abdul Khader Jeelan
S Nadiya
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
S Afrin Taj
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
Y Jaswanth Reddy
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
V Siva Praneeth Reddy
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
V C Sai Vignesh
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
S Abdul Khader Jeelan
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences (Autonomous) Kadapa, Andra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15267161

Published 2025-04-23

Keywords

  • Federated Learning,
  • Lung Disease Diagnosis,
  • Decentralized Data Sharing,
  • Encrypted Model Updates,
  • Healthcare Data Security

How to Cite

S Nadiya, S Afrin Taj, Y Jaswanth Reddy, V Siva Praneeth Reddy, V C Sai Vignesh, & S Abdul Khader Jeelan. (2025). Securing Patient Data With Blockchain Enabled Federated Learning For Medical Diagnostics. Milestone Transactions on Medical Technometrics, 3(2), 201–213. https://doi.org/10.5281/zenodo.15267161

Abstract

The proposed framework introduces a groundbreaking solution in healthcare by integrating Federated Learning (FL) with blockchain technology for diagnosing lung diseases. Traditional machine learning models often rely on centralized data collection, raising concerns about patient data privacy and potential misuse of sensitive information. FL tackles this issue by allowing hospitals to collaborate in training machine learning models without sharing raw patient data. Each hospital processes its local data independently, sending encrypted model updates instead, ensuring that data privacy is preserved while fostering collective innovation. To further strengthen this approach, blockchain technology is employed to securely encrypt and immutably store the shared model updates, creating a transparent and tamper-proof system. This combination not only addresses privacy concerns but also builds trust and accountability among participating hospitals. Significantly, the framework empowers smaller hospitals and under-resourced medical centers to contribute to and benefit from advanced diagnostic capabilities. By pooling resources through FL and ensuring equitable access via blockchain, it reduces disparities between healthcare providers, enabling improved diagnostics in distant or underserved areas. To the best of your knowledge, this represents the first practical implementation of blockchain-empowered FL on such diverse medical data, making a substantial contribution to the integration of artificial intelligence, blockchain, and healthcare. With its potential to revolutionize collaborative diagnostics while prioritizing privacy and security, this framework sets a new standard for technological innovation in medicine
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