Articles Open Access
Federated Machine Learning for Enterprise Data Sharing Across Hybrid Cloud Environments with Privacy-Preserving Mechanisms
College of Computers and Information Technology, Taif University, Saudi Arabia
Abstract
Federated machine learning (FML) has emerged as a promising approach for enabling collaborative data analysis across enterprises while preserving data privacy and security. In hybrid cloud environments, organizations distribute their data across private data centers, public cloud platforms, and edge infrastructure, creating challenges related to data governance, interoperability, regulatory compliance, and secure information sharing. Traditional centralized machine learning approaches require transferring data to a common repository, increasing exposure to privacy breaches and unauthorized access. Federated learning addresses these limitations by enabling participating organizations to train shared machine learning models locally and exchange model parameters or updates instead of raw data. However, federated learning introduces additional risks, including model inversion attacks, gradient leakage, communication overhead, and vulnerabilities associated with untrusted participants. This study examines a privacy-preserving federated machine learning framework designed for enterprise data sharing across hybrid cloud environments. The proposed methodology integrates secure aggregation, differential privacy, encryption, and adaptive cloud resource management to strengthen confidentiality while maintaining model utility. A conceptual experimental framework is developed to evaluate predictive accuracy, communication efficiency, computational overhead, privacy protection, and scalability under varying network and participation conditions. The study also considers interoperability and governance requirements to support secure collaboration among heterogeneous enterprise systems. The proposed approach aims to establish a scalable and trustworthy foundation for privacy-aware enterprise intelligence, enabling organizations to derive collective insights without relinquishing direct control over their sensitive data
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 5 |
| Pages | 17586-17595 |
| Published | 2025-10-18 |
| Issue | Vol. 9 No. 5 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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33) Srikanth, V., Giridhar, M. S., Madhuri, J. V., & Thangappan, H. (2025). Sustainable waste management through blockchain and Internet of Things integration in industrial zones. International Journal of Environmental Sciences, 11(8s), 2025.
34) Bitragunta, S. L. V. (2024). AI-driven deep learning-based animal intrusion detection and early warning system for railroad tracks. International Research Journal of Innovative Engineering, 8(1), 13909–13918.
35) Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
36) Gadige, C. D. (2023). Secure CRM modernization: Architectural patterns for migrating enterprise customer data from legacy CRM platforms to Salesforce. International Journal of Science, Research and Technology, 6(3), 9946–9956.
37) Parupalli, A. (2025, August). Improving Cloud Resource Utilization and Cost Efficiency Through Data-Driven ML Algorithms. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1–8). IEEE.
38) Shaik, N. (2025). Securing kubernetes: An integrated approach to ai-driven threat detection and EBPFbased security monitoring. International Journal of Research and Applied Innovations, 8(2), 11289–11294.
2) Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Research Journal of Innovative Engineering, 8(4), 14994–15009.
3) 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.
4) Selvarajan, K. (2025). Petabyte-scale data processing for real-time enterprise security analytics. International Research Journal of Innovative Engineering (IRJIE), 9(5), 17566–17576.
5) Kundavaram, R. R., Onteddu, A. R., & Kishore, K. K. (2024). Smart banking: The impact of artificial intelligence on financial services. Economic Sciences, 20(1), 193–202.
6) Shaik, N. (2023). Zero downtime, zero trust: Automating certificate security at scale. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 8(4), 13973–13988.
7) Pothuri, M. K. (2025). From legacy systems to scalable cloud platforms: Building modern data pipelines with data engineering strategies, scaling trust, compliance, and performance in public health. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 13(5). https://doi.org/10.37082/IJIRMPS.v13.i5.232724
8) Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
9) Padmanabham, S. (2025). Security and Compliance in Integration Architectures: A Framework for Modern Enterprises. International Journal of Computing and Engineering, 7(16), 45–55.
10) Kollu, R. K. (2023). CRM architecture deep dive: Building for scale on Salesforce. International Journal of Engineering & Extended Technologies Research, 5(5), 7309–7312.
11) Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231–244). Singapore: Springer Nature Singapore.
12) Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
13) Ahuja, D. (2025, August). Intelligent Failure Prediction in CI/CD Pipelines Using Efficient Machine Learning Techniques. In 2025 5th Asian Conference on Innovation in Technology (ASIANCON) (pp. 1–7). IEEE.
14) Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
15) Bandari, U., & Subramani, S. K. (2025). AI-ready data products: Extending data contracts and semantic layers for LLM and agent consumption. International Journal of Computer Technology and Electronics Communication, 8(4), 11214–11224.
16) Gunasekaran, R. M. N. (2024). AI/ML and Generative AI Use Cases in Financial Services. The Eastasouth Journal of Information System and Computer Science, 2(02), 345–356.
17) Chinnam, N. B. (2025). Intelligent distribution center integration for transportation modernization. International Journal of Research and Applied Innovations, 8(2), 11283–11288.
18) Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925–4930.
19) Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3–15.
20) Basavala, S. R. (2024). Automated vulnerability intelligence and risk prioritization for secure CI/CD pipeline engineering. International Journal of Future Innovative Science and Technology (IJFIST), 7(1), 12057–12067.
21) Sahu, S. (2024). Digital Governance Framework for Salesforce Data Cloud in Healthcare Insurance Platforms. International Journal of Innovations in Science, Engineering And Management, 120–128.
22) 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.
23) Puram, S. (2021). Reliable and Scalable Mobile Technology Architecture: A Formal Model, Synchronization Protocol, and Reference Design. The USA Journals TAJET, 3(08), 33–48.
24) 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.
25) Neela, S. (2024). Low-latency, high-throughput middleware for real-time enterprise integration: Technical design, societal impact, and policy considerations. International Journal of Emerging Research in Engineering and Technology, 5(3), 191–200. https://doi.org/10.63282/3050-922X.IJERET-V5I3P121
26) Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
27) Reddy, K. V. (2023). Reconfigurable FPGA-based acceleration of RTL simulation and co-simulation for cloud datacenter workloads. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13265.
28) Enayet, M. A., Sultana, S., Khan, M. A. N., Jakir, T., Tasnim, M., & Alam, A. (2025). Comparative Analysis of Quantum Kernel Methods and Deep Neural Networks for Heart Disease Prediction. Vascular and Endovascular Review, 8(10s), 394–409.
29) Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15394–15406.
30) Soundappan, S. J. (2022). Distributed Cloud Architecture for Intelligent Performance Optimization and Proactive Real-Time Threat Detection. International Research Journal of Innovative Engineering, 6(6), 11426–11432.
31) 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.
32) 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.
33) Srikanth, V., Giridhar, M. S., Madhuri, J. V., & Thangappan, H. (2025). Sustainable waste management through blockchain and Internet of Things integration in industrial zones. International Journal of Environmental Sciences, 11(8s), 2025.
34) Bitragunta, S. L. V. (2024). AI-driven deep learning-based animal intrusion detection and early warning system for railroad tracks. International Research Journal of Innovative Engineering, 8(1), 13909–13918.
35) Ramasamy, M. (2023). Cloud-native control plane design for modern network automation systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9082–9091.
36) Gadige, C. D. (2023). Secure CRM modernization: Architectural patterns for migrating enterprise customer data from legacy CRM platforms to Salesforce. International Journal of Science, Research and Technology, 6(3), 9946–9956.
37) Parupalli, A. (2025, August). Improving Cloud Resource Utilization and Cost Efficiency Through Data-Driven ML Algorithms. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1–8). IEEE.
38) Shaik, N. (2025). Securing kubernetes: An integrated approach to ai-driven threat detection and EBPFbased security monitoring. International Journal of Research and Applied Innovations, 8(2), 11289–11294.
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