Federated Deep Learning Enabled Smart Healthcare Monitoring and Predictive Analytics Framework
Abstract
The rapid growth of healthcare data from wearable devices, IoT sensors, and electronic health records demands intelligent systems for real-time monitoring and prediction. However, centralized data processing raises privacy and security concerns. This paper proposes a Federated Deep Learning-enabled Smart Healthcare Monitoring and Predictive Analytics Framework that enables distributed model training without sharing raw patient data. The framework integrates edge computing, deep learning models, and federated learning protocols to ensure privacy-preserving collaborative intelligence across healthcare institutions. It enhances disease prediction accuracy, supports early diagnosis, and enables continuous patient monitoring while complying with data protection regulations such as HIPAA and GDPR.
Article Information
Journal |
International Journal of Emerging Trends in Engineering and Management Research |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) |
DOI |
|
Pages |
14320-14326 |
Published |
September 4, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Johan Bakker (%2023). Federated Deep Learning Enabled Smart Healthcare Monitoring and Predictive Analytics Framework. International Journal of Emerging Trends in Engineering and Management Research , Vol. 8 No. 5 (2023): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 14320-14326. https://doi.org/10.15662/ijetemr.2023.0805001 |
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36. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
37. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
38. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
39. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
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41. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
42. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
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2. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
3. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
4. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
5. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
6. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
7. Mathew, A. (2019). Cybersecurity infrastructure and security automation. Adv Comput: Int J (ACIJ), 10(6).
8. Sugumar, R. (2018). Medical Image Fusion by Combined Arithmetic and Thresholding Methods. EDITORS OF SPECIAL ISSUE JOURNAL, 17.
9. Balasubramanian, V., & Rajendran, S. (2019). Rough set theory-based feature selection and FGA-NN classifier for medical data classification. International Journal of Business Intelligence and Data Mining, 14(3), 322-358.
10. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
11. Mathew, A. R. Malware Analysis of API Calls using FPGA Hardware Level Security.
12. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
13. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
14. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
15. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
16. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
17. Adepu, R. (2023). Designing FedRAMP-Compliant Cloud Architectures for Secure and Scalable Government Systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 10427-10441.
18. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
19. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977
20. Katta, T. B. (2022). A Capability Maturity Framework for Event-Driven Integration: Benchmarking Kafka and Pulsar in Enterprise Environments. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9589.
21. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
22. Shewale, V. (2023). AI and Machine Learning for Anomaly Detection in ICS Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(3), 11631.
23. Parasa, M. (2023). Measuring skill graph drift in SAP SuccessFactors Talent Intelligence Hub for career mobility, workforce reskilling, and skills-based talent governance. Advanced International Journal of Multidisciplinary Research, 1(1), 1–27. https://doi.org/10.62127/aijmr.2023.v01i01.1359
24. Subramanyam, S. P. (2023). Cloud infrastructure automation and role-based access governance in Azure Kubernetes services. International Journal of Research Publications in Engineering, Technology and Management, 6(2), 8392–8400.
25. Namdeo, A. (2023). Generative synthetic data pipelines for bias-free BI training. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(1), 10818–10826. https://doi.org/10.15662/IJAESIT.2023.0601003
26. Panyala, V. R. (2022). Engineering event-driven microservices platforms for real-time data processing in cloud ecosystems. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 34–48.
27. Pasumarthi, H. (2023). Applying machine learning to high-volume banking platforms: From transaction data to predictive risk intelligence. International Journal of Computer Technology and Electronics Communication, 6(4), 7352–7356
28. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715-3724.
29. Vankayala, S. C. (2023). Governed Autonomy in Reliability Engineering: Integrating Error Budgets with AI-Driven Remediation. J Artif Intell Mach Learn & Data Sci 2023, 1(2), 3191-3196.
30. Adepu, G. (2022). Graph AI–Driven Environmental Intelligence Platforms for Predictive Regulatory Risk Assessment. International Journal of Computer Technology and Electronics Communication, 5(5), 5776-5780.
31. Kanji, R. K. (2022). Generative Query Optimization in Data Warehousing: A Foundation Model-Based Approach for Autonomous SQL Generation and Execution Optimization in Hybrid Architectures. Available at SSRN 5401216.
32. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
33. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
34. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
35. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
36. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
37. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
38. Brijet, Z., & Bharathi, N. (2021). Design of type-2 fuzzy logic controller for fluid catalytic cracking unit. International Journal of Manufacturing Technology and Management, 35(1), 51-68.
39. Mathew, A. R. Airport Cyber Security and Cyber Resilience Controls. arXiv 2019. arXiv preprint arXiv:1908.09894.
40. Sulthana, A. R., & Murugeswari, B. (2011, March). ARIPSO: Association rule interactive postmining using schemas and ontologies. In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (pp. 941-946). IEEE.
41. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
42. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
43. Suresh, T., Brijet, Z., & Sheeba, T. B. (2021). CMVHHO-DKMLC: A Chaotic Multi Verse Harris Hawks optimization (CMV-HHO) algorithm based deep kernel optimized machine learning classifier for medical diagnosis. Biomedical Signal Processing and Control, 70, 103034.