Cloud-Enabled Federated Intelligence Frameworks for Secure Data Collaboration across Distributed Enterprise Platforms
DOI:
https://doi.org/10.15662/IJEETR.2026.0805003Keywords:
Federated intelligence, Federated learning, Cloud computing, Secure data collaboration, Privacy preservation, Enterprise platforms, Distributed analytics, Secure aggregation, Data governance, Multi-cloud securityAbstract
The increasing distribution of enterprise data across cloud platforms, organizational boundaries, geographic regions, and heterogeneous information systems has created significant challenges for secure data collaboration and intelligent analytics. Conventional centralized data-processing approaches require organizations to transfer sensitive datasets to common repositories, creating concerns related to privacy, regulatory compliance, data sovereignty, security, and communication overhead. Cloud-enabled federated intelligence provides an alternative approach in which organizations retain data within their respective environments while collaboratively training or executing intelligent models through secure coordination mechanisms. This paper proposes a cloud-enabled federated intelligence framework for secure data collaboration across distributed enterprise platforms. The framework integrates federated learning, cloud orchestration, privacy-preserving computation, secure aggregation, identity and access management, policy enforcement, and intelligent model coordination. Rather than transferring raw enterprise data, participating platforms exchange protected model parameters, encrypted updates, or derived knowledge under predefined collaboration policies. The framework incorporates adaptive resource allocation and trust-aware coordination to address heterogeneous infrastructure, unreliable participants, communication constraints, and potential adversarial behavior. A closed-loop governance mechanism continuously evaluates privacy, security, model performance, and policy compliance throughout the collaboration lifecycle. The proposed research methodology evaluates the framework using distributed enterprise scenarios involving healthcare, finance, supply-chain, and multi-cloud analytics. Performance is assessed using model accuracy, convergence time, communication overhead, privacy protection, computational cost, security resilience, and policy-compliance measures. The framework aims to demonstrate how cloud-based federated intelligence can facilitate collaborative analytics while preserving organizational control over sensitive data
References
1. Nekkalapudi, K. (2026, June). Building Scalable Microservices with Java, Spring Boot, Kafka, and Kubernetes on AWS. In 2026 5th International Conference on Computer Networks, Big Data and IoT (ICCBI) (pp. 704-710). IEEE.
2. Zhou, S., Wang, L., Wu, W., & Feng, L. (2025). Joint client selection and resource allocation for federated edge learning with imperfect CSI. Computer Networks, 257, 110914. https://doi.org/10.1016/j.comnet.2024.110914
3. Batzner, J., Nelaturu, S. H., Stachura, D., Kornilova, A., Crall, J., Cerruti, T., ... & Choshen, L. (2026). Every Eval Ever: A Unifying Schema and Community Repository for AI Evaluation Results. arXiv preprint arXiv:2606.14516.
4. Bitragunta, S. L. V. (2024). Intelligent electric vehicle charging hubs AI-based demand forecasting and smart energy management. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15383–15393.
5. Sharma, K., Singhal, A. B., karthik Chundi, V. R., & Arveti, S. (2026, February). Analyzing Interdependencies Between IoT Data Integration Capabilities and Real-Time Operational Decision-Making: A DEMATEL Approach. In 2026 5th International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1-5). IEEE.
6. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
7. Tatavarthi, S., Bikkavolu, V., Gulapalli, S., Koilakonda, R. R., & Divi, V. R. (2026, July). HybridFraudNet: A Dual-Branch CNN-GNN Architecture for Healthcare Insurance Fraud Detection. In 2026 Seventeenth International Conference on Ubiquitous and Future Networks (ICUFN) (pp. 917-922). IEEE.
8. 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.
9. Polamarasetty, V. K. (2026). Enterprise HR technology modernization through global Workday implementation and legacy system consolidation. International Journal of Research and Applied Innovations, 9(1), 13691–13696.
10. Mohile, A., Kumara, S., Sivashanmugam, S. P., & Mathur, S. (2026, April). A Machine Learning-Driven Framework for Rapid Cybersecurity Incident Response and Mitigation. In 2026 International Conference on Connected Intelligence for Industrial Applications (CI2A) (pp. 1-6). IEEE.
11. Maroju, P. K., & Mudunuri, L. N. R. (2025, October). Generative AI as a Cyber Weapon: Synthetic Identity Fraud and Phishing at Scale. In International Conference on Artificial Intelligence and Networking (pp. 573-585). Cham: Springer Nature Switzerland.
12. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications, 9, 36-43.
13. Chinnam, N. B. (2024). Cross-network scheduling optimization for unified retail distribution networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8183–8188.
14. 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.
15. Hashmi, H., & Srikanth, V. (2023, November). Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 694-697). IEEE.
16. Goyal, K. K., Hebbar, K. S., & Mali, R. K. (2026, March). AI Modernization Enabled by Cloud-Native and Edge-Intelligent Architectures. In International Conference on Information Technology and Artificial Intelligence (pp. 102-114). Cham: Springer Nature Switzerland.
17. 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.
18. Rekulapalli, K., Kagga, S. R., Ayyagari, V., Akula, A., & Raj Akula, R. (2026). AI in HRM: Enhancing workforce competency and organizational effectiveness. Proceedings of the International Conference on Innovative Computing and Communication (ICICC 2026).
19. 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.
20. Attaluri, V. C., Hullurappa, M., Batchu, R., Mudunuri, L. N. R., Vemulapalli, G., & Palakurti, N. R. (2025, April). Identity Access Management for Network Devices: Enhancing Security of Modern IT Infrastructure in Healthcare. In 2025 International Conference on Data Science and Business Systems (ICDSBS) (pp. 1-7). IEEE.
21. Pothuri, M. K. (2025). Building Self-Service BI in the Cloud with AI Integration: Power BI and Snowflake. International Journal of Emerging Trends in Computer Science and Information Technology, 256-262.
22. Ganesan, N., & Kandhasamy, V. (2026). Early Detection of Cotton Plant Diseases Using Zebra Optimizer with Deep Learning Approach. Traitement du Signal, 43(1), 519.
23. 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.
24. 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
25. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
26. Khare¹, A., & Goyal, A. K. (2026, May). Integrating Artificial Intelligence and Big Data in Supply Chain Management for Increased Efficiency and Sustainability. In Proceedings of Fifth International Conference on Computing and Communication Networks: ICCCN 2025, Volume 4 (Vol. 4, p. 447). Springer Nature.
27. Ramasamy, M. (2024). Secure and extensible platform architectures for enterprise network orchestration. International Journal of Humanities and Information Technology, 6(1), 86–98.
28. Srikanth, V., Walia, R., Augustine, P. J., Simla, J., & Jegajothi, B. (2022, March). Chaotic Whale Optimization based Node Localization Protocol for Wireless Sensor Networks Enabled Indoor Communication. In 2022 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 702-707). IEEE.
29. Shaik, N. P. (2025). Automated TLS certificate lifecycle management: A policy-driven framework for Kubernetes security hardening. International Journal of Computer Technology and Electronics Communication, 8(2), 10497–10502.
30. Yang, et al. (2024). Information interaction and partial growth-based multi-population growable genetic algorithm for multi-dimensional resources utilization optimization of cloud computing. Swarm and Evolutionary Computation, 87, 101575. https://doi.org/10.1016/j.swevo.2024.101575.
