Federated and Autonomous AI Systems for Privacy-Preserving Cloud and Enterprise Intelligence
DOI:
https://doi.org/10.15662/IJEETR.2026.0802455Keywords:
Federated Learning, Autonomous AI, Privacy-Preserving Systems, Cloud Computing, Enterprise Intelligence, Distributed Learning, Data Security, Decentralized Systems, AI Governance, Edge ComputingAbstract
Federated and autonomous artificial intelligence (AI) systems are emerging as transformative paradigms for enabling privacy-preserving intelligence across cloud and enterprise environments. Traditional centralized AI models require aggregating vast amounts of sensitive data into a single repository, raising concerns related to data privacy, regulatory compliance, and security vulnerabilities. Federated learning addresses these challenges by allowing decentralized data sources to collaboratively train models without sharing raw data, thereby preserving confidentiality while maintaining model performance. Autonomous AI systems further enhance this framework by incorporating self-governing capabilities such as adaptive learning, decision-making, and minimal human intervention, enabling scalable and efficient deployment in dynamic enterprise ecosystems.
This research explores the integration of federated and autonomous AI architectures within cloud infrastructures, highlighting their potential to support secure data analytics, distributed intelligence, and enterprise-level decision-making. It also examines challenges such as communication overhead, model heterogeneity, trust management, and system robustness. By combining privacy-preserving techniques with intelligent automation, these systems provide a viable solution for organizations seeking to leverage data-driven insights while adhering to strict data governance policies. The study concludes that federated and autonomous AI systems represent a critical advancement toward secure, scalable, and intelligent enterprise computing.
References
1. Gopinathan, V. R. (2025). Software engineering practices for AI-driven systems: From development to deployment (MLOps perspective). International Journal of Science, Research and Technology, 8(1), 13493-13500.
2. Gowda, M. K. S. (2026). Next-Gen Risk Frameworks ML Integration for Credit Monitoring and Governance. International Journal of Science, Research and Technology, 9(2), 435-443.
3. Sugumar, R. (2025). Federated AI in Offline-First Mobile Health Architectures for Privacy-Preserving Clinical Intelligence. International Journal of Science, Research and Technology, 8(4), 14589-14600.
4. Niture, N. (2023). Machine Learning and Cryptographic Algorithms--Analysis and Design in Ransomware and Vulnerabilities Detection. Authorea Preprints.
5. Narayanan, S. (2025). Autonomous cyber sovereignty: A dual-control architecture for agentic artificial intelligence in offensive defensive security ecosystems. World Journal of Advanced Research and Reviews, 25(3), 2538–2546.
6. Upadhyaya, P., Chettier, T. M., Boyina, V. A. K., & Pradhan, C. (2025). MCP agents for automated cloud compliance and governance. International Journal on Recent and Innovation Trends in Computing and Communication, 13(1), 205–214. https://www.researchgate.net/profile/Thiyagarajan-Mani-Chettier/publication/395268734_MCP_Agents_for_Automated_Cloud_Compliance_and_Governance/links/68ba0479df4c076e62fd7958/MCP-Agents-for-Automated-Cloud-Compliance-and-Governance.pdf
7. Murugeshwari, B., Selvaraj, D., Sudharson, K., & Radhika, S. (2023). Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography. Intelligent Automation & Soft Computing, 35(1).
8. Soundappan, S. J. (2025). Next Generation AI Enabled Holistic Cognitive Platform for Secure Cloud Network Intelligence Enterprise Systems and Digital Trust Optimization. International Journal of Computer Technology and Electronics Communication, 8(5), 11534-11542.
9. Bheemisetty, N. (2026). Next-Gen Data Ecosystems: Domain-AI across Spark, ETL, and Batch Intelligence. International Journal of Science, Research and Technology, 9(2), 382-390.
10. Mathew, A., & Alex, H. (2022). Detect & protect-medical device cybersecurity. Curr. Overview Sci. Technol. Res, 1, 60-68.
11. 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.
12. Mudusu, S. K. (2025). AI-driven data engineering in the Internet of Things: Scaling data pipelines for smart device ecosystems. ISCSITR-International Journal of Data Engineering (ISCSITR-IJDE), 6(1), 1–9.
13. Vigenesh, M. (2025). Autonomous Operational Resilience across AI Guided Cloud Platforms with Proactive Threat Mitigation. International Journal of Technology, Management and Humanities, 11(03), 108-115.
14. Dave, B. L. (2023). FEDERATED AI FRAMEWORKS FOR REGULATED INDUSTRIES: CROSS-DOMAIN INTELLIGENCE FOR SOCIAL SERVICES, INSURANCE, AND INDUSTRIAL OPERATIONS. International Journal of Research and Applied Innovations, 6(1), 8346-8362.
15. Tiwari, S. K. (2025). Automating Behavior-Driven Development with Generative AI: Enhancing Efficiency in Test Automation. Frontiers in Emerging Computer Science and Information Technology, 2(12), 01-14.
16. Gentyala, R. (2026). Distinguishing Chaos from Corruption: Differentiating Systemic Market Drift from Byzantine Poisoning in Heterogeneous Federated Learning Environments for Credit Risk. Journal ID, 9471, 1297.
17. Adepu, G. (2025). AI-based epidemiological data platforms for early outbreak detection and real-time health analytics. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 9–29.
18. Rengarajan, A., Mishra, A., Kulhar, K. S., Shrivastava, V. P., & Alawneh, Y. J. J. (2024, March). Role of Deep Reinforcement Learning in Mitigating Cyber Security Issues: A Review. In International Conference on Renewable Power (pp. 37-48). Singapore: Springer Nature Singapore.
19. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
20. Sammy, F., Chettier, T., Boyina, V., Shingne, H., Saluja, K., Mali, M., ... & Shobana, A. (2025). Deep Learning-Driven Visual Analytics Framework for Next-Generation Environmental Monitoring. Journal of Applied Science and Technology Trends, 114-122.
21. 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.
22. Boddupally, H. L. (2018). Secure data governance for enterprise reporting: A governance-layer model for SSRS-based architectures. Journal of Artificial Intelligence, Machine Learning & Data Science, 1(1), 3148-3153.
23. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
24. Umasankar, P. (2025). Advanced Unified AI Cognitive Ecosystem for Adaptive Cloud Network Security Intelligent Enterprise Transformation and Self Healing Data Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11209-11217.
25. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta 2 (1):21-27.
26. 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.
27. Yamsani, N. (2017). Enterprise-Scale Data Stewardship Enablement Using Workflow-Driven Governance Mechanisms in Financial Services. International Journal of Technology, Management and Humanities, 3(01), 18-31.
