Skip to main content

Federated Learning for Privacy-Preserving Healthcare Analytics across Distributed Enterprise Data Ecosystems

Dr. Ismail Shahin

Professor, University of Sharjah, United Arab Emirates

Abstract

Federated learning (FL) has emerged as an important approach for enabling collaborative machine learning across distributed healthcare organizations while reducing the need to centralize sensitive patient data. Healthcare enterprises generate large volumes of heterogeneous information through electronic health records, medical imaging, laboratory systems, wearable devices, clinical applications, and administrative platforms. Conventional centralized analytics requires organizations to transfer data to a common repository, creating substantial privacy, security, regulatory, and governance concerns. Federated learning addresses this challenge by allowing participating institutions to train machine-learning models locally and share model updates rather than raw patient records. This architecture can support collaborative analytics while retaining sensitive information within institutional boundaries. However, healthcare federated learning introduces challenges involving data heterogeneity, communication efficiency, model convergence, privacy leakage, malicious participants, unequal computational resources, and regulatory accountability. This study investigates a privacy-preserving federated-learning framework for healthcare analytics across distributed enterprise data ecosystems. The proposed methodology establishes participating healthcare data nodes, develops a common machine-learning task, applies local model training and secure aggregation, and evaluates the resulting global model against centralized and conventional distributed-learning alternatives. Privacy, predictive performance, communication overhead, computational cost, convergence behavior, and robustness against selected security threats are considered evaluation dimensions. The study aims to determine how federated learning can support collaborative healthcare intelligence while maintaining institutional control over sensitive data and strengthening privacy-aware enterprise analytics

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0806027

Pages

15532-15540

Published

December 18, 2025
Copyright

Open Access

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

How to Cite

Dr. Ismail Shahin (%2025). Federated Learning for Privacy-Preserving Healthcare Analytics across Distributed Enterprise Data Ecosystems. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15532-15540. https://doi.org/10.15662/IJSRAT.2025.0806027

References

1. Jiang, T., Shen, G., Guo, C., Cui, Y., & Xie, B. (2023). BFLS: Blockchain and Federated Learning for sharing threat detection models as Cyber Threat Intelligence. Computer Networks, 224, 109604. https://doi.org/10.1016/j.comnet.2023.109604
2. Lingala, B. (2025). Optimizing Distributed Database Performance Across Geographical Boundaries. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12818-12826.
3. Mudunuri, L. N. R., Maroju, P. K., & Aragani, V. M. (2025, January). Leveraging nlp-driven sentiment analysis for enhancing decision-making in supply chain management. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-6). IEEE.
4. 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.
5. Gaddam, M. K., Kapoor, S., Vedula, J., Manu, M., Usmani, M. A., & Polvanov, S. (2025, July). LiDAR Sensor Simulation in Unity: Real-Time Performance for Autonomous Systems and Virtual Environments. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
6. Anand, L. (2023). Machine Learning Enabled Enterprise Integration through Intelligent API Governance Secure Cloud Infrastructure and Automated Operations. International Journal of Research and Applied Innovations, 6(3), 5972-5979.
7. Ahuja, D. (2024). An overview of OpenShift architecture and enterprise container platform management. Journal of Science Engineering Technology and Management Science, 1(1), 224–232.
8. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
9. Khare, A., Bhatnagar, A., & Singh, K. (2025, October). Adoption of SAP SuccessFactors in Talent Analytics and Workforce Forecasting. In 2025 2nd International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT) (pp. 1-6). IEEE.
10. Ramasamy, M. (2024). Telemetry-driven network operations: Architectures for analytics and automated remediation. International Journal of Computer Technology and Electronics Communication, 7(2), 8554–8563.
11. Chundi, V. R. K. (2025). AI-Powered Sustainability Integration: Transforming Retail and Manufacturing Through Enterprise Resource Planning Solutions. Journal of Computer Science and Technology Studies, 7(5), 881-887.
12. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
13. Bellundagi, M. (2023). Design of an Intelligent Clinical Decision Support System Using Machine Learning Techniques. International Journal of Research and Applied Innovations, 6(6), 10075-10081.
14. Manda, P. (2024). Oracle E-Business Suite modernization: The transition from 12.1.3 to 12.2.8. International Journal of Research and Applied Innovations, 7(3), 10838–10842.
15. Sanku, B. S., Li, Y. J., & He, J. S. (2023, May). A survey of VR-based neurofeedback systems in physiological computing for depression treatment. In 2023 9th International Conference on Virtual Reality (ICVR) (pp. 369-377). IEEE.
16. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
17. Ambalakannu, M. (2025). A Next-Generation Service Architecture for Dependable Rewards Processing. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11598-11606.
18. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
19. Balaraman, N. K., Patel, K., Mahendran, P. K. R., & Kasarla, N. R. (2025, August). AI Driven Predictive Maintenance in Water and Wastewater Systems: Enhancing Efficiency, Reliability, and Sustainability. In 2025 IEEE 16th Control and System Graduate Research Colloquium (ICSGRC) (pp. 93-98). IEEE.
20. Kalakoti, M. K. R. (2025). Provider-agnostic infrastructure as code: A modular framework for secure multi-tenant cloud automation. Journal of international crisis and risk communication research, 188-197.
21. Bandhela, R. R., & Kannan, V. (2021). Leveraging generative AI and large language models for secure and efficient healthcare data management. Journal of Informatics Education and Research, 1(3). https://jier.org/index.php/journal/article/view/4350
22. Addepalli, D. N. V., Chen, J., & Xie, C. Y. (2023, October). Molecular Dynamics Simulations on Coronavirus Variants of Concern (VOC). In Proceedings of the 24th Annual Conference on Information Technology Education (pp. 213-215).
23. Agarwal, S. (2025). Event-driven self-healing infrastructure: A conceptual framework for intelligent automation in site reliability engineering. Journal of Information Systems Engineering and Management, 10(57s), 539–549.
24. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
25. Koganti, H. (2024). The computational complexity of hybrid algorithms: When branch-and-bound meets machine learning heuristics. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11184–11190.
26. Vineetha, B., Surendran, R., & Madhusundar, N. (2024, November). Enhancing accuracy in obesity prediction and nutrition guidance through KNN and decision tree models. In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI) (pp. 757-762). IEEE.
27. Pothuri, M. K. Building a Seamless Healthcare Data Fabric: Zero-Touch Integration and Scalable Mapping Across Provider, Claims, Recipient, and Pharmacy Source Systems for State Medicaid. IJLRP-International Journal of Leading Research Publication, 6(8).
28. Reddy, K. V. (2024). Accelerating Functional Coverage Closure Through Iterative Machine Learning. Int. J. Comput. Appl. Inf. Technol, 7, 1401-1411.
29. 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.
30. Sanku, B. S. (2026). AI-Driven Adaptive E-Learning Using Real-Time Frustration Detection and Content Difficulty Adjustment. International Journal of Artificial Intelligence and Computer Electronics, 2(1), 69-79.
31. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2025). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
32. Badam, L. R. (2024). AI-based early warning system for financial scams targeting consumers. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10593-10602.
33. Selvarajan, K. (2024). Observability-driven reliability engineering for distributed data platforms. International Journal of Computer Technology and Electronics Communication, 7(4), 9258–9268. https://doi.org/10.15680/IJCTECE.2024.0704019
34. Gaddam, M. K., Kapoor, S., Vedula, J., Manu, M., Usmani, M. A., & Polvanov, S. (2025, July). LiDAR Sensor Simulation in Unity: Real-Time Performance for Autonomous Systems and Virtual Environments. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
35. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
36. Polamarasetty, V. K. (2023). Modern enterprise HR technology through Workday-based benefits and absence management. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6908–6913.
37. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3-15.
38. Sandhu, Y. S. (2024). Real time ETL optimization using change data capture incremental. International Journal of Emerging Trends in Engineering and Management Research, 9(3), 15711–15721.
39. Jeyaraj, S. A. L., Kumar, S., Revathy, S., Yenigalla, G., Krishna, K. B., & Jayabalan, K. (2024). Machine Learning Algorithms for E-Commerce Security: A Practical Approach. In Strategies for E-Commerce Data Security: Cloud, Blockchain, AI, and Machine Learning (pp. 361-385). IGI Global Scientific Publishing.
40. Zhu, K., Chen, W., Jiao, L., Wang, J., Peng, Y., & Zhang, L. (2023). Online training data acquisition for federated learning in cloud–edge networks. Computer Networks, 223, 109556. https://doi.org/10.1016/j.comnet.2023.109556