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Secure and Decentralized Heart Sound Analysis using Federated Learning and Blockchain Technology
Research Article | Published: 16 November 2025
ICCK Transactions on Machine Intelligence Volume 2, Issue 1, 2026: 1-11
Volume 2, Issue 1, ICCK Transactions on Machine Intelligence
Volume 2, Issue 1, 2026
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ICCK Transactions on Machine Intelligence, Volume 2, Issue 1, 2026: 1-11

Free to Read | Research Article | 16 November 2025
Secure and Decentralized Heart Sound Analysis using Federated Learning and Blockchain Technology
by
Shivani Agarwal Shivani Agarwal 1
Meenakshi Kandpal Meenakshi Kandpal 2
Vishan Kumar Gupta Vishan Kumar Gupta 3 *
Shubhani Aggarwal Shubhani Aggarwal 4
Paras Jain Paras Jain 5
1 Ajay Kumar Garg Engineering College, Ghaziabad, Uttar Pradesh, India
2 Odisha University of Technology and Research, Bhubaneswar, Odisha, India
3 Amity School of Engineering & Technology, Amity University Punjab, Mohali, Punjab, India
4 University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
5 VIT Bhopal University, Kothrikalan, Sehore, Madhya Pradesh, India
* Corresponding Author: Vishan Kumar Gupta, [email protected]
DOI: 10.62762/TMI.2025.567350
Received: 05 October 2025, Accepted: 30 October 2025, Published: 16 November 2025  
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Abstract
Early diagnosis of cardiac abnormalities depends on accurate classification of heart sounds, but centralized training methods run the danger of violating patient privacy. We thus propose a privacy-preserving and reliable heart sound abnormality detection system combining Blockchain Technology with Federated Learning (FL). Training is spread among seven clients, each simulating an independent data source, using a preprocessed dataset from the PhysioNet Challenge 2016 to enable distributed learning without sharing raw data. CNN-LSTM model using FedAvg achieved the best performance: 94\% accuracy, 0.90 precision, 0.96 recall, and an AUC of 0.98 among five deep learning architectures evaluated with FedAvg and FedProx strategies. Along with metadata including client ID and round number, SHA-256 hashes of local and global model weights were recorded on a local Ethereum blockchain following every communication round to guarantee model integrity. The hash of the final model is revalidated against the blockchain to confirm authenticity prior to deployment. It then guarantees safe, distributed, clinically valuable AI-based diagnostics by real-time classification of heart sounds as normal or abnormal.
Graphical Abstract
Secure and Decentralized Heart Sound Analysis using Federated Learning and Blockchain Technology
Keywords
federated learning
blockchain-based model validation
heart sound classification
privacy-preserving artificial intelligence
CNN-LSTM architecture
SHA-256 model integrity
decentralized healthcare diagnostics
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Agarwal, S., Kandpal, M., Gupta, V. K., Aggarwal, S., & Jain, P. (2025). Secure and Decentralized Heart Sound Analysis using Federated Learning and Blockchain Technology. ICCK Transactions on Machine Intelligence, 2(1), 1–11. https://doi.org/10.62762/TMI.2025.567350
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