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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68232
Volume:
10 |
10 |
Oct 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Engineering
Sandip Institute of Technology and Research Centre , Nashik
Department of Computer Engineering
Sandip Institute of Technology and Research Centre , Nashik
Department of Computer Engineering
Sandip Institute of Technology and Research Centre , Nashik
Department of Computer Engineering
Sandip Institute of Technology and Research Centre , Nashik
Department of Computer Engineering
Sandip Institute of Technology and Research Centre , Nashik
Healthcare artificial intelligence is still commonly built by pooling patient data from every hospital on a single server and training a model there. This approach delivers accuracy but creates a serious privacy risk, and it conflicts with regu-lations such as HIPAA, GDPR and India’s Digital Personal Data Protection Act, which is a major reason why hospitals are reluctant to release raw records [1], [2]. This paper pro-poses a system that avoids centralization instead of work-ing within it. Wearable devices at each hospital node con-tinuously collect physiological signals (heart rate, ECG, SpO2 and blood pressure), and a Long Short-Term Memory (LSTM) model [3] is trained on these data locally and on-site. Only an encrypted set of model weights leaves the hospital; a central server combines the weights using Fed-erated Averaging (FedAvg) [4] and returns an improved global model. Over 20 communication rounds, the global model reaches 97.6% accuracy and a 97.2% F1-score for early disease prediction over five categories, which is with-in about half a percentage point of a centralized model trained with full access to the same pooled data. Clinicians interact with the system through a Streamlit dashboard that shows prediction confidence, patient trends and alerts without exposing another hospital’s raw records.
KEYWORDS
The proposed work combines IoT and Federated Learning to provide secure and privacy-preserving healthcare monitoring. It allows healthcare data to remain on local devices while a machine-learning model is trained collaboratively. This approach reduces the privacy risks associated with centralized data storage and provides a scalable solution for intelligent healthcare monitoring [1], [12]. The system can help improve data security, patient privacy and monitoring efficiency. Future work can focus on real-time monitoring, multimodal healthcare data [17], stronger privacy techniques such as secure aggregation [33] and differential privacy [34], and large-scale deployment [24].
1. Dasaradharami Reddy, K., Gadekallu, T.R.: A comprehensive survey on federated learning techniques for healthcare informatics. Comput. Intell. Neurosci. 2023, Art. 8393990 (2023) 2. Abaoud, M., Almuqrin, M.A., Khan, M.F.: Advancing federated learning through novel mechanism for privacy preservation in healthcare applications. IEEE Access 11 (2023) 83562–83579 3. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8) (1997) 1735–1780 4. McMahan, B., Moore, E., Ramage, D., Hampson, S., Agüera y Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: Proc. 20th Int. Conf. Artif. Intell. Statist. (AISTATS) (2017) 5. Baig, M.M., Gholamhosseini, H.: Smart health monitoring systems: An overview of design and modeling. J. Med. Syst. 37(2), Art. 9898 (2013) 6. Baker, S.B., Xiang, W., Atkinson, I.: Internet of Things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access 5 (2017) 26521–26544 7. Taha, Z.K., Yaw, C.T., Koh, S.P., Tiong, S.K., Kadirgama, K., Benedict, F., Tan, J.-D., Balasubramaniam, Y.: A survey of federated learning from data perspective in the healthcare domain: Challenges, methods, and future directions. IEEE Access 11 (2023) 45711–45735 8. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521 (2015) 436–444 9. Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. N. Engl. J. Med. 380 (2019) 1347–1358 10. Topol, E.J.: High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 25 (2019) 44–56
Publication Date
10/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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