Skip to main content
Articles

Privacy-Preserving Federated AI Frameworks for Secure Data Collaboration across Distributed Enterprise Cloud Platforms

Đỗ Đình Thanh

Faculty of Information Technology, Ho Chi Minh University of Foreign Languages and Information Technology (HUFLIT), Vietnam

Abstract

 Privacy-preserving federated artificial intelligence (AI) provides an emerging approach for enabling collaborative machine learning across distributed enterprise cloud platforms without requiring organizations to directly centralize sensitive datasets. Conventional centralized AI architectures often require data to be transferred to common repositories, creating concerns regarding confidentiality, regulatory compliance, data ownership, and security. Federated learning addresses these concerns by allowing participating organizations or cloud environments to train models locally and exchange model updates rather than raw data. However, model updates can still expose sensitive information, while malicious participants, insecure communication channels, and heterogeneous data distributions can affect the reliability of collaborative learning. This study proposes a privacy-preserving federated AI framework for secure data collaboration across distributed enterprise cloud platforms. The framework integrates federated learning with encryption, secure aggregation, differential privacy, participant authentication, access control, anomaly detection, and model governance. The research methodology employs a distributed experimental environment representing multiple enterprise organizations with heterogeneous datasets and cloud infrastructures. Evaluation focuses on model accuracy, communication overhead, convergence time, privacy protection, resilience against malicious participants, and computational cost. Different privacy configurations are compared to examine the trade-off between privacy strength and learning performance. The proposed framework aims to establish a secure and scalable foundation for collaborative AI in environments where data cannot be freely centralized, while maintaining organizational control, auditability, and compliance throughout the federated learning lifecycle

Article Information

Journal

International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

Volume (Issue)

Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

DOI

https://doi.org/10.15662/IJAESIT.2024.0705010

Pages

14937-14944

Published

October 10, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Đỗ Đình Thanh (2024). Privacy-Preserving Federated AI Frameworks for Secure Data Collaboration across Distributed Enterprise Cloud Platforms. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 5 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 14937-14944. https://doi.org/10.15662/IJAESIT.2024.0705010

References

1. Kairouz, P., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. https://doi.org/10.1561/2200000083
2. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
3. Bandari, U., & Thilagaraj, S. V. (2022). From central BI to federated self-service: A platform-as-a-product reference architecture for data mesh for a multinational manufacturer. International Journal of Science, Research and Technology, 5(3), 7784–7794.
4. Badam, L. R. (2022). Machine learning-based catastrophic loss prediction for climate-related insurance risk. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7797–7807.
5. Ambalakannu, M. (2024). Driving operational efficiency and clinical insights via unified care management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10693-10702.
6. Shalk, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
7. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
8. Sandhu, Y. S. (2024). Real time ETL optimization using change data capture incremental. International Journal of Emerging Trends in Engineering and Management Research, 9(3), 15711–15721.
9. Devineni, A., Pochincharla, S., & Muppalla, L. K. (2022). A telemetry-fusion framework for proactive incident detection in multi-tenant financial cloud platforms. International Journal of Advances in Signal and Image Sciences, 13–18.
10. 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.
11. Mishra, D., Behera, D., Challa, A., & Vemu, C. (2021). PDCloudEX software defined network. In Integrated emerging methods of artificial intelligence & cloud computing (pp. 66–70). Springer International Publishing.
12. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
13. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology, 5(3), 7777-7783.
14. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
15. 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.
16. Ahuja, D. (2023). Multi-Cloud Management Strategies: Tools, Challenges, and Implementation Methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493-506.
17. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
18. 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.
19. Neela, S. (2022). Semantic Interoperability in Real-Time Enterprise Integration Using Middleware Abstractions. American International Journal of Computer Science and Technology, 4(6), 56-66.
20. Selvarajan, K. (2024). Modernizing legacy big data platforms using cloud-native lakehouse architectures. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 10377–10385.
21. Basavala, S. R. (2021). Threat modeling and security requirements specifications for enterprise application development. International Research Journal of Innovative Engineering, 5(1), 7498–7508.
22. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
23. 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.
24. Kundavaram, R. M. R. (2022). Cloud-based data analysis identifying key financial influencers at scale. European Economic Letters (EEL), 12(2), 234–241. https://www.eelet.org.uk/index.php/journal/article/view/3389
25. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
26. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
27. Yadavalli, V. R. (2022). Continuous trust scoring for cross-layer access governance in S/4HANA, Fiori, and SAP BTP. International Journal of Engineering & Extended Technologies Research, 4(1), 4347–4357.
28. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
29. Tarigoppula, S. (2023). Adaptive cloud cost optimization through predictive auto-scaling and infrastructure intelligence. International Journal of Future Innovative Science and Technology, 6(2), 10278–10290.
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. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
32. Gudipuri, N. (2024). Intent-Aware Enterprise Architecture Using Persistent AI Agents for Dynamic FinTech Service Composition. The Eastasouth Journal of Information System and Computer Science, 2(01), 151-161.
33. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
34. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
35. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
36. Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775. https://doi.org/10.1016/j.knosys.2021.106775