Federated AI Learning across Cloud Environments for Enterprise Data Privacy and Distributed Intelligence Networks
DOI:
https://doi.org/10.15662/IJARCST.2026.0903021Keywords:
Federated Learning, Artificial Intelligence, Enterprise Data Privacy, Multi-Cloud Computing, Distributed Intelligence, Machine Learning, Secure Aggregation, Privacy-Preserving AI, Cloud Computing, Data Governance, Decentralized Learning, Enterprise AI, Differential Privacy, Model Security, Distributed NetworksAbstract
Federated Artificial Intelligence (AI) learning across cloud environments is emerging as an important approach for organizations seeking to develop intelligent systems without centralizing sensitive enterprise data. Traditional machine-learning architectures generally require data to be transferred to a central repository where models are trained, creating concerns related to privacy, security, regulatory compliance, data ownership, and communication overhead. Federated learning addresses these limitations by allowing AI models to be trained across distributed data environments while keeping the original data within its respective location. In an enterprise context, data may be distributed across multiple public clouds, private clouds, edge environments, branch offices, data centers, and organizational departments. Federated AI enables these geographically and technologically distributed environments to collaborate by exchanging model parameters, gradients, or other learned information instead of raw datasets. This research examines a federated AI architecture designed for multi-cloud enterprise environments and focuses on how distributed intelligence can improve privacy while maintaining analytical capabilities. The proposed methodology incorporates cloud-based federated coordination, local model training, secure aggregation, privacy-preserving mechanisms, communication management, model validation, and governance. The architecture supports heterogeneous enterprise data sources while allowing organizations to retain control over their information. The study argues that federated AI can provide a foundation for privacy-aware distributed intelligence networks by combining machine learning with decentralized data management. The proposed approach is particularly relevant to enterprises operating under strict privacy, security, and data-residency requirements, where conventional centralized AI architectures may introduce significant operational and compliance risks
References
1. Bheemisetty, N. (2026). The structural shift: How unified claims platforms reshape payer economics. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 4763–4769.
2. Chaba, A. (2018). A platform-independent API integration architecture for scalable enterprise commerce solution. International Journal of Research Publication in Engineering, Technology and Management, 1(1), 9–13.
3. Hossain, M. D., Akter, F., Rahaman, M., Biswas, B., & Hasan, H. M. (2026). Hierarchical Federated Learning with Heavy-Hitter Detection for Real-Time Cyber Threat Mitigation in Large-Scale Networks. Available at SSRN 6340898.
4. Gopinathan, V. R. (2025). Revolutionizing Revenue Cycle Management in the US Healthcare System Using AI-Powered Cloud Solutions. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11118.
5. Narapareddy, V. S. R., & Yerramilli, S. K. (2022). Scaling the ServiceNow CMDB for distributed infrastructures. International Journal of Engineering Technology Research & Management, 6(10), 101–113.
6. Sugumar, R. (2023, September). A Novel Approach to Diabetes Risk Assessment Using Advanced Deep Neural Networks and LSTM Networks. In 2023 International Conference on Network, Multimedia and Information Technology (NMITCON) (pp. 1-7). IEEE.
7. Ambati, K. C. (2026). End-to-end procurement architecture from requisition to analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 246–251.
8. Tyagi, N. (2025). Signal & Oversight: Machine Intelligence Meets Financial Regulation. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12490-12498.
9. 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.
10. Prasad, A. (2025). Monitoring and analyzing latency and performance in ultra low latency environments powered by RDMA. International Journal of Applied Mathematics, 38
11. Kilari, K. K. (2026). Cybersecurity awareness and behavioral outcomes in healthcare organizations. International Journal of Drug Delivery Technology, 16(58s), 816–823.
12. Raja, G. V. (2023). Modernizing enterprise systems using AI with machine learning and cloud computing for intelligent systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
13. Konapalli, Y., Othmane, L. B., Tunc, C., Benchellal, F., & Shivaraj, L. M. (2026, April). Reverse Engineering and Control-Aware Security Analysis of the ArduPilot UAV Framework. In Proceedings of the 8th International Workshop on Software Engineering Research and Practices for the IoT (pp. 98-106).
14. Kanji, R. K. (2021). Real-Time Big Data Processing with Edge Computing. European Journal of Advances in Engineering and Technology, 8(11), 152-155.
15. Mohammad Ali, M. A., Md Shahadat Hossain, M. S. H., Md Whahidur Rahman, M. W. R., & Md Shahdat Hossain, M. S. H. (2025). AI-Driven Predictive Modeling to Detect and Prevent Financial Fraud in US Digital Payment Systems. AI-Driven Predictive Modeling to Detect and Prevent Financial Fraud in US Digital Payment Systems, 5(12), 228-255.
16. 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.
17. Ambalakannu, M. (2026). Building a unified benefits data repository for real-time eligibility and enrollment processing. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 4770–4775.
18. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
19. Wadhwa, R. (2025). A DevOps-oriented approach to enterprise systems engineering with event-driven microservices and distributed data systems. International Journal of Microservices and Applications, 3(1), 22–34. https://doi.org/10.34218/IJMA_03_01_003
20. Mathew, A. (2023). Sentinel AI: An Investigation into Robust Threat Mitigation Strategies for Artificial Intelligence. Educational Research (IJMCER), 5(5), 108-111.
21. Indurthy, V. S. K. (2026). A modern approach to asset management: Integrating SimCorp Dimension with cloud data platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 4757–4762.
22. Juvvadi, R. R. (2025). Toward autonomous finance: A multi-agent system architecture for the self-driving financial close. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11290–11295.
23. Challa, R. (2024). High-availability HPC cluster design for mission-critical public infrastructure: Lessons from energy grid and government deployments. Computer Fraud & Security, 2024(11), 444–454
24. Ayyagari, V., & Kagga, S. R. (2025). Using Denodo and Google Pub/Sub for Unified Data Access Across Distributed Healthcare Systems. European Journal of Electrical Engineering and Computer Science, 9(6), 20-27.
25. Bhagwat, V. B. (2025). Simplifying Payroll Balance Conversions in Payroll Systems Implementation through the Use of Generative AI.
26. Akter, K. S., Onik, T. A., Yasin, M., Nabil, M. A., Hossain, I., & Akter, S. (2024). AI-Driven Early Detection of Chronic Kidney Disease: A Comparative Benchmark of Ensemble Learning Models with Clinical Explainability. Vascular and Endovascular Review, 7(2), 480-493.
27. Raja, G. V. (2023). AI Driven Secure Intelligent Framework for Fraud Detection Cybersecurity and Cloud Based Enterprise Systems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(5), 9068-9076.
28. Korat, U., & Alimohammad, A. (2026, January). Efficient Hardware Implementation of Eigen Solver. In 2026 IEEE 16th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 1376-1385). IEEE.
29. Gowda, M. K. S. (2026). Optimizing regulatory compliance with machine learning: Boosting accuracy and efficiency. International Journal of Research and Applied Innovations (IJRAI), 9(1), 13686–13690.
30. Vas, M. R. (2025). Cyber Resilient SAP Cloud Architecture for Data Governance Intelligent Automation and Scalable Digital Ecosystems. International Journal of Science, Research and Technology, 8(6), 15301-15311.
31. Macha, Y. (2022). A Review of Cloud-Based CRM Systems in Healthcare: Advances, Tools, Challenges, and Best Practices. Int. J. Curr. Eng. Technol, 12(6), 848-856.
32. Soundappan, S. J. (2023). AI-Driven Secure Enterprise Analytics and Intelligent Cloud Data Management Frameworks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 6(3), 8236-8242.
33. Pokala, H. K. (2021). Carbon-aware Kubernetes scheduling with sustainable GitOps and energy-efficient DevOps for green cloud computing practices. Journal of Computational Analysis and Applications, 29(6), 1497-1506.
34. Kilari, K. K. (2026). Cybersecurity awareness and behavioral outcomes in healthcare organizations. International Journal of Drug Delivery Technology, 16(58s), 816–823.
35. Meesala, A. (2023). Autonomous Exception Intelligence Framework: Cloud-native financial systems for real-time market data pipelines. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11768.
36. Bellundagi, M. (2025). DevOps transformation in enterprise environments. International Journal of Science, Technology and Convergence, 7(7).
37. Somu, B. (2022). Modernizing core banking infrastructure: The role of AI/ML in transforming IT services. Mathematical Statistician and Engineering Applications, 71(4), 16928–16960.
38. Gopinathan, V. R. (2025). Revolutionizing Revenue Cycle Management in the US Healthcare System Using AI-Powered Cloud Solutions. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11118.
39. Ambati, K. C. (2026). End-to-end procurement architecture from requisition to analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 246–251.
40. Tyagi, N. (2025). The Compliance Horizon: Anticipating Regulatory Change in Financial Services and Artificial Intelligence. International Journal of Research and Applied Innovations, 8(2), 11227-11232.
