Articles Open Access
Developing Federated AI Frameworks for Distributed Cloud Enterprise Data Governance and Secure Intelligence
Independent Researcher, United Kingdom
Abstract
Modern multi-national enterprises heavily rely on distributed cloud environments to process vast amounts of telemetry, transaction logs, and customer interactions. However, centralized artificial intelligence (AI) training paradigms introduce significant data privacy risks, regulatory compliance hurdles, and prohibitive bandwidth overheads. This paper explores the architectural design and implementation of Federated AI Frameworks tailored for distributed cloud infrastructures to achieve robust enterprise data governance and secure intelligence. By decoupling model training from raw data access, federated learning (FL) enables local enterprise nodes to train AI models on-site and transmit only encrypted gradients or model updates to a global aggregation server. We propose a enterprise-grade federated framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy ($\epsilon, \delta$), and blockchain-based audit logging to ensure traceably governance across hybrid and multi-cloud systems. Our framework addresses critical challenges, including system heterogeneity, non-IID (non-independently and identically distributed) data skew, and adversarial parameter poisoning. Experimental evaluations demonstrate that our framework maintains up to 94.2% model accuracy relative to centralized baselines while reducing cross-region egress traffic by over 78% and guaranteeing strict adherence to global privacy mandates like GDPR and HIPAA.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 4 |
| Pages | 17119-17126 |
| Published | 2025-08-06 |
| Issue | Vol. 9 No. 4 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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26. Juvvadi, R. R. (2024). Embedding ESG and carbon accounting into the financial ledger: A sub-ledger architecture for CSRD-aligned reporting. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(1), 7531–7535.
27. Seetala, S. R. (2022). Intelligent Data Validation in Modern Data Platforms: Integrating Statistical Methods and AI for Reliable Machine Learning Pipelines. J Artif Intell Mach Learn & Data Sci, 5(2), 3359-3366.
28. 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.
29. Gopisetty, S. (2024). The Watchful Guardian That Never Says “Pause”: A Self-Supervised AI Framework for Real-Time Compliance Auditing in High-Velocity Fintech MLOps. Journal ID, 9471, 1297.
30. 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.
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32. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
2. Meesala, A. (2024). Enterprise-Wide Outstanding Management Platform: AI and Cloud-Native Platform for Real-Time Governance Visibility in Financial Infrastructure. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), 357-363.
3. Anand, L., & Neelanarayanan, V. (2020, October). Enchanced multiclass intrusion detection using supervised learning methods. In AIP conference proceedings (Vol. 2282, No. 1, p. 020044). AIP Publishing LLC.
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. Mohammed, S. (2023). Modernizing enterprise service desk and EUC operations with AI-powered automation. International Journal of Innovative Research in Computer and Communication Engineering, 11(12), 12235–12244. https://doi.org/10.15680/IJIRCCE.2023.1112044
6. Gujarathi, M. (2024). State management strategies for high-frequency mobile enterprise applications. International Journal of Science, Research and Technology (IJSRAT), 7(5), 12869–12878.
7. Kanji, M. R. K. (2022). A Unified Data Warehouse Architecture for Multi-Source Forest Inventory Integration and Automated Remote Sensing Analysis. Journal Of Engineering And Computer Sciences, 1(5), 10-16.
8. Bhakuni, G., Srinivas, S., Rao, S., Ayyalusamy, G. K., Nakka, S., & Kumar, S. (2025, May). Object Detection and Localization in Real-Time Using Image Processing and Deep Learning. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1-7). IEEE.
9. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
10. Gopisetty, S. (2024). The Watchful Guardian That Never Says “Pause”: A Self-Supervised AI Framework for Real-Time Compliance Auditing in High-Velocity Fintech MLOps. Journal ID, 9471, 1297.
11. Chaganti, S. (2024). A unified MLOps and data architecture blueprint for cross-enterprise decisioning in global financial and tourism ecosystems. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 601–611. https://ijisae.org/index.php/IJISAE/article/view/7960
12. Kale, P. (2024). AI-Augmented DevSecOps Pipelines for Secure and Efficient Software Delivery in Cloud-Native Platforms. International Journal of Emerging Research in Engineering and Technology, 5(3), 201-209.
13. Umasankar, P., & Saravanan, K. (2016). Artificial Neural Network based Smart Charging Systems for Lead Acid Batteries. Asian Journal of Research in Social Sciences and Humanities, 6(cs1), 181-191.
14. Meesala, L. K. (2023). Modern security information and event management: Architecture, analytics pipelines, and empirical evaluation of SOC-scale threat detection. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN, 2456-3307.
15. Polamreddy, V. R. (2024). Designing enterprise data migration frameworks for continuous business operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8161–8164.
16. Mondal, K. K., Rahman, R., Bipasha, Y. A., & Kadir, A. (2023). Polygenic hazard score and amyloid PET imaging mediation analysis in Alzheimer's disease diagnosis. International Journal of Science and Research Archive, 10(2), 1451–1457. https://doi.org/10.30574/ijsra.2023.10.2.0980
17. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
18. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
19. Vasa, M. R. (2023). A Reconciliation-Driven Methodology for Legacy-to-Cloud Data Warehouse Migration: A Framework for the Enterprise Reporting Platform. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 175-182.
20. Narayanan, S. (2022). Transforming cybersecurity with AI-driven dashboards: A cloud-native implementation framework for real-time threat detection and automated response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.
21. Mudusu, S. K. (2025). Data Engineering Challenges in AI-Driven Healthcare IT Systems: Navigating Real-Time Analytics and Interoperability.
22. Hossain, M. S., Ali, M., & Haider, P. S. (2022). Generative AI and Predictive Business Analytics for Early Detection of Cybersecurity Threats in Enterprise Networks. American Journal of Economics and Business Management, 5(12).
23. Sugumar, R. (2024). AI-Driven Cloud Framework for Real-Time Financial Threat Detection in Digital Banking and SAP Environments. International Journal of Technology, Management and Humanities, 10(04), 165-175.
24. 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.
25. Raghothama Rao, G. (2024). When simplicity outscales cleverness in software architecture. Computer Fraud and Security, 2024(4). https://computerfraudsecurity.com/index.php/journal/article/view/942
26. Juvvadi, R. R. (2024). Embedding ESG and carbon accounting into the financial ledger: A sub-ledger architecture for CSRD-aligned reporting. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(1), 7531–7535.
27. Seetala, S. R. (2022). Intelligent Data Validation in Modern Data Platforms: Integrating Statistical Methods and AI for Reliable Machine Learning Pipelines. J Artif Intell Mach Learn & Data Sci, 5(2), 3359-3366.
28. 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.
29. Gopisetty, S. (2024). The Watchful Guardian That Never Says “Pause”: A Self-Supervised AI Framework for Real-Time Compliance Auditing in High-Velocity Fintech MLOps. Journal ID, 9471, 1297.
30. 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.
31. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
32. Anbazhagan, R. S. K. (2016). A Proficient Two Level Security Contrivances for Storing Data in Cloud.
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