Articles
Federated AI and Cloud Computing Models for Cross-Enterprise Risk Intelligence
Abstract
To facilitate federated AI and federated cloud architectures for cross-enterprise Risk Intelligence in 2025, the study synthesizes its aims and scope, establishing relevance and impact. Cross-border Risk Intelligence engages insights from distinct Economies for local Governance use. Analysis emissions Cross-Border Information Act compliance, especially at non-country cartel jurisdiction breaks. Cross-Economy Data Management Agency amid disparate Risk Assurance requirements, perpetual audit, activity-based Cross-Economy Digital Services Levy, and unalignment with foreign standards risk disintegration of worldwide trade. Distorted governance dash move to afford globalisation shortcuts via far-from-market subvention need Countervailing Duties—economically falling prove hards through regulatory action, indeed guising phase modulation . Cross-enterprise Risk Analytics thus examine Capital-Source Reporting Standard Data Residency. Preventing dissatisfaction then demand Cross-Border-Compliance-Are Insistence—protocol shopping demand is collapse of a tough wall before regulator. Federated Cloud Multi Cloud and Hybrid Cloud Platform-Are Insight Demand. Latency-Sensitive Federation-Proximity Need Advance Deployment of Cloud-Capacity Engine for Real.Time Data Pre-Processing-Streaming-Risk Signal-Online-Offline Fusion. Domain-/Risk-Signature Stemming Edge-Cloud Edge for Excess-Rate-Deplorable Distance Latency-Sensitive Applications Finally-Are Imperatives Hence-Force Federated Cloud—Federal Cloud—Federated Infrastructure-Are Interest Demand in All.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17756-17772 |
Published |
December 9, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dileep Valiki (2025). Federated AI and Cloud Computing Models for Cross-Enterprise Risk Intelligence. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17756-17772. https://doi.org/10.15662/IJAESIT.2025.0806008 |
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29. Sun, Y., Xie, J., Guo, Z., et al. (2024). Secure cross-enterprise federated learning for financial risk prediction. IEEE Transactions on Dependable and Secure Computing, 21(2), 980–993.
30. Kalisetty, S., & Ganti, V. K. A. T. (2019). Transforming the Retail Landscape: Srinivas’s Vision for Integrating Advanced Technologies in Supply Chain Efficiency and Customer Experience. Online Journal of Materials Science, 1, 1254.
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32. Varri, D. B. S. (2024). Adaptive and Autonomous Security Frameworks Using Generative AI for Cloud Ecosystems. Available at SSRN 5774785.
33. Zhao, L., Chen, R., & Xu, D. (2025). Federated AI orchestration for global enterprise risk intelligence platforms. IEEE Transactions on Services Computing, 18(1), 112–125.
34. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376.
35. Wang, Y., Liu, H., & Zhang, J. (2025). Cross-jurisdictional privacy-preserving AI frameworks for regulatory-compliant risk analytics. Information Systems Frontiers, 27(2), 289–304.
36. Vajpayee, A., Khan, S., Gottimukkala, V. R. R., Sharma, D., & Seshasai, S. J. (2025). Digital Financial Literacy 4.0: Consumer Readiness for AI-Driven Fintech and Blockchain Ecosystems. International Insurance Law Review, 33(S5), 963-973.
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