Federated Artificial Intelligence and Cloud Computing Frameworks for Secure Digital Transformation Strategies

Mohammed Wajid; Mohammed Wajid

doi:10.15662/IJAESIT.2023.0605003

Articles

Federated Artificial Intelligence and Cloud Computing Frameworks for Secure Digital Transformation Strategies

Mohammed Wajid

Senior Engineer, ConnectiveRx, North Carolina, United States

Abstract

The accelerating pace of digital transformation has driven organizations to adopt advanced technologies such as artificial intelligence (AI) and cloud computing to remain competitive and agile. However, the increasing reliance on centralized data processing raises significant concerns regarding data privacy, security, and regulatory compliance. Federated Artificial Intelligence (FAI) has emerged as a promising paradigm that enables collaborative model training without requiring the transfer of sensitive data across organizational boundaries. This study explores the integration of federated AI with cloud computing frameworks to develop secure and scalable digital transformation strategies. The proposed framework leverages distributed learning mechanisms, privacy-preserving techniques, and cloud-native infrastructures to ensure data confidentiality while enabling intelligent decision-making. By combining federated learning with secure cloud environments, organizations can achieve enhanced data utilization, reduced risk of data breaches, and improved compliance with data protection regulations. The research highlights the architectural design, implementation strategies, and performance evaluation of such frameworks, demonstrating their effectiveness in real-world enterprise scenarios. The findings contribute to the development of resilient, privacy-aware, and intelligent digital ecosystems, offering a sustainable pathway for organizations seeking to balance innovation with security in the era of data-driven transformation.

Article Information

Journal	International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
Volume (Issue)	Vol. 6 No. 5 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)
DOI	https://doi.org/10.15662/IJAESIT.2023.0605003
Pages	12440-12448
Published	October 10, 2023
Copyright	Creative Commons Attribution 4.0 International License
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Mohammed Wajid (2023). Federated Artificial Intelligence and Cloud Computing Frameworks for Secure Digital Transformation Strategies. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 5 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 12440-12448. https://doi.org/10.15662/IJAESIT.2023.0605003

References

1. Parupalli and S. Pandya, “Compliance-Driven Data Governance : A Survey on GDPR , and HIPAA in Cloud Databases,” vol. 12, no. 6, pp. 828–836, 2022, doi: 10.14741/ijcet/v.12.6.18.
2. Kunadi, S. K. (2023). Entity resolution at scale: Advanced fuzzy matching techniques for company and project data. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8014–8022.
3. Balamuralidhar Sarabu, V. (2020). Scalable data processing patterns for national retail platforms: An enterprise architecture for high-volume transaction systems. International Journal of Computer Technology and Electronics Communication (IJCTEC), 3(3), 1–14.
4. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
5. Karvannan, R. (2023). Empowering healthcare operations with next-generation compliance and inventory solutions. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 297–313.
6. Ali, M., Hossain, M. S., Rahman, M. W., & Hossain, M. S. (2022). Leveraging Business Analytics to Enhance Supply Chain Resilience and Reduce Disruptions in Critical US Industries. Journal of Business and Management Studies, 4(4), 239-263.
7. 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.
8. Gentyala, R. (2023). Anticipating Clinical Decay: A Meta-Learning Framework for Proactive Drift Detection and Feature Attribution in Deployed Healthcare AI. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 198-216.
9. Nallamothu, T. K. (2023). Generative AI in healthcare: Automating clinical documentation, diagnostics, and knowledge synthesis. International Journal of Computer Technology and Electronics Communication, 6(1), 6376–6392.
10. Padala, S. (2021). Cloud-Enabled AI Contact Centers in Oncology Care. International Journal of AI, BigData, Computational and Management Studies, 2(3), 93-98.
11. Mallireddy, S. (2021). How impactful tools like ServiceNow and Power BI in financial and mother baby units. International Journal of Future Innovative Science and Technology, 4(1), 1–6.
12. Vankayala, S. C. (2019). Establishing Auditable and Privacy-Respectful Test Data Systems through Synthetic Data Engineering and Governance-Driven Anonymization. International Journal of Computer Technology and Electronics Communication, 2(6), 1809-1821.
13. Adepu, R. (2021). Modernizing legacy data centers through virtualization and software-defined infrastructure. International Journal of Research and Applied Innovations (IJRAI), 4(4), 17–36.
14. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
15. 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.
16. 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.
17. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
18. Kandan, M., Krishnamurthy, A., Selvi, S. A. M., Sikkandar, M. Y., Aboamer, M. A., & Tamilvizhi, T. (2022). Quasi oppositional Aquila optimizer-based task scheduling approach in an IoT enabled cloud environment. The Journal of Supercomputing, 78(7), 10176-10190.
19. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT based under ground cable fault detection with cloud storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
20. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
21. 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.
22. Alam, M. K., Fahad, M. L. R., & Miah, N. (2023). A data-driven analysis of how AI-driven misinformation and deepfakes affect public trust in US financial institutions. Journal of Computer Science and Technology Studies, 5(1), 133-160.
23. Thumala, S. R. (2022). Importance of Business Continuity and Disaster Recovery (BCDR) Methodologies for Organizations: A Comparison Study between AWS and Azure. International Journal of Science and Research (IJSR), 11(12), 1406-1415.
24. Jagannathan, P., Gurumoorthy, S., Stateczny, A., Divakarachar, P. B., & Sengupta, J. (2021). Collision-aware routing using multi-objective seagull optimization algorithm for WSN-based IoT. Sensors, 21(24), 8496.
25. Myakala, P. K. (2022). Adversarial robustness in transfer learning models. Iconic Research And Engineering Journals, 6(1), 772-779.
26. Narayanan, S. (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
27. Yamsani, N. (2022). Applying Machine Learning for Automated Data Quality and Anomaly Detection in Enterprise Data Pipelines. International Journal of Research and Applied Innovations, 5(1), 9457-9466.
28. Mathew A R, Al Zahli J A. Cloud Technology and the Challenges for Forensics InvestigatorsJ. DEStech Transactions on Computer Science and Engineering, 2017 (cnsce).
29. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.
30. Lanka, S. (2023). Blurring boundaries where artificial intelligence ends and human potential begins. International Journal of Computer Technology and Electronics Communication, 6(4), 7331–7341.
31. Joyce, S. (2023). Optimizing SAP workloads on cloud-native platforms: A framework for intelligent resource allocation and performance scaling. International Journal of Science, Research and Technology (IJSRAT), 6(1), 9210–9219. https://doi.org/10.15662/IJSRAT.2023.0601002
32. Subramanyam, S. P. (2022). Kubernetes-oriented continuous deployment architecture for .NET microservices. International Journal of Future Innovative Science and Technology (IJFIST), 5(3), 8482–8490. https://doi.org/10.15662/IJFIST.2022.0503002
33. Namdeo, A. (2022). Federated learning BI across multi-cloud data silos. The International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7893–7903.
34. Panyala, V. R. (2023). AI-augmented DevOps frameworks for accelerating cloud-native platform engineering at scale. International Journal of Research and Applied Innovations, 6(1), 8375–8379.
35. Pasumarthi, H. (2023). A Deep Dive into Enterprise B2B Integrations: Designing High-Availability File and API Workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
36. Boddupally, H. L. (2020). Human-Centered Experience Engineering through Cognitive Design Patterns in Web-Based Systems. International Journal of Computer Technology and Electronics Communication, 3(6), 2909-2922.
37. 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.
38. Adepu, G. (2021). AI-enabled digital identity verification framework for government self-service platforms using secure API and cloud integration. International Journal of Research Publications in Engineering, Technology and Management, 4(1), 160–176.
39. Raja, G. V. (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.