Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems

Dr.R.Sugumar; Dr.R.Sugumar

doi:10.15662/IJFIST.2022.0505005

Articles

Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems

Dr.R.Sugumar

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

The rapid digital transformation of healthcare systems has significantly increased the adoption of cloud computing, artificial intelligence, Internet of Medical Things devices, and distributed healthcare analytics platforms. Modern healthcare ecosystems continuously generate massive volumes of sensitive patient data from hospitals, wearable devices, diagnostic systems, telemedicine platforms, electronic health records, and medical imaging systems. However, centralized healthcare data processing approaches often create major challenges related to patient privacy, cybersecurity risks, regulatory compliance, data interoperability, and scalability limitations. Federated Learning has emerged as a transformative distributed AI paradigm that enables collaborative machine learning across multiple healthcare institutions without directly sharing sensitive patient data. This research presents a comprehensive framework for Federated Learning and Distributed AI Architectures in cloud-based cyber healthcare data collaboration systems. The proposed framework integrates federated machine learning, distributed cloud infrastructure, intelligent cybersecurity mechanisms, privacy-preserving analytics, blockchain-supported governance, and real-time healthcare intelligence platforms to support secure and scalable collaborative healthcare analytics. The framework enables healthcare organizations to train AI models collaboratively while maintaining data privacy, regulatory compliance, and decentralized operational control. Experimental analysis demonstrates improvements in predictive healthcare analytics, data privacy protection, distributed scalability, intelligent diagnosis accuracy, and cybersecurity resilience. The findings indicate that federated learning-driven healthcare architectures provide secure, scalable, privacy-aware, and intelligent solutions for future cloud-based collaborative healthcare ecosystems.

Article Information

Journal	International Journal of Future Innovative Science and Technology (IJFIST)
Volume (Issue)	Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST)
DOI	https://doi.org/10.15662/IJFIST.2022.0505005
Pages	9218-9232
Published	September 9, 2022
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr.R.Sugumar (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST) , Vol. 5 No. 5 (2022): International Journal of Future Innovative Science and Technology (IJFIST) , pp. 9218-9232. https://doi.org/10.15662/IJFIST.2022.0505005

References

1. Mathew, A. (2020). Threat intelligence and internet of medical things (IoMT). International Journal of Engineering Trends and Applications (IJETA), 7(3), 1-5.
2. Kunadi, S. K. (2021). Establishing robust data foundations: Early-stage architecture for scalable data warehousing and analytics systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3078–3088.
3. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.
4. Vankayala, S. C. (2021). Engineering Quality into Cloud-Native Financial Platforms on Microsoft Azure. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(1), 4361-4367.
5. 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.
6. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
7. Adepu, G. (2021). AI-enabled digital identity verification framework for government self-service platforms using secure API and cloud integration. International Journal of Research Publications in Engineering, Technology and Management, 4(1), 160–176.
8. Anand, L., & Syed Ibrahim, S. P. (2018). HANN: a hybrid model for liver syndrome classification by feature assortment optimization. Journal of medical systems, 42(11), 211.
9. Mallireddy, S. (2021). Data encryption and policies via digital transformations and services. International Journal of Research and Applied Innovations, 4(5), 1–6.
10. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).
11. Revathi, K. G., Ananth, B. J., Saravanan, M. L., & Kumar, A. R. (2021). Gps enabled vehicle location identification using gsm and fare collection using smart card. Turkish journal of computer and mathematics education, 12(10), 2657-2668.
12. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
13. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
14. Begum, R. S., & Sugumar, R. (2016). Conditional entropy with swarm optimization approach for privacy preservation of datasets in cloud [J]. Indian Journal of Science and Technology, 9(28).
15. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
16. Udayakumar, S. Y. P. D. (2023). Real-time migration risk analysis model for improved immigrant development using psychological factors.
17. Boddupally, H. L. (2020). Enterprise-scale data quality improvement using machine learning: Frameworks, validation strategies, and operational insights. Validation Strategies, and Operational Insights (August 31, 2020).
18. Yamsani, N. (2019). Engineering trustworthy enterprise data through structured validation and cleansing controls: Insights from Elavon data quality operations. International Journal of Science, Engineering and Technology, 7(1). Zenodo.https://doi.org/10.5281/zenodo.18194337
19. Jagannathan, P., Gurumoorthy, S., Stateczny, A., Divakarachar, P. B., & Sengupta, J. (2021). Collision-aware routing using multi-objective seagull optimization algorithm for WSN-based IoT. Sensors, 21(24), 8496.
20. Adepu, R. (2021). Modernizing legacy data centers through virtualization and software-defined infrastructure. International Journal of Research and Applied Innovations (IJRAI), 4(4), 17–36.
21. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
22. Balamuralidhar Sarabu, V. (2020). Scalable data processing patterns for national retail platforms: An enterprise architecture for high-volume transaction systems. International Journal of Computer Technology and Electronics Communication (IJCTEC), 3(3), 1–14.
23. Wen, B., Li, Y., & Bresler, Y. (2020). Image recovery via transform learning and low-rank modeling: The power of complementary regularizers. IEEE Transactions on Image Processing, 29, 5310-5323.
24. Tohfa, N. A., Hossain, I., Zareen, S., Rasul, I., Hossen, M. S., & Rahman, M. (2021). Adversarial Cognition Machine Learning at the Frontlines of Cyber Warfare. World Journal of Advanced Research and Reviews, 12(02), 722-729.
25. Subramani, V. (2022). Architectural Approaches for Securing Cloud Native Microservices. International Journal of Computer Technology and Electronics Communication, 5(3), 5169-5176.
26. 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.
27. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).