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Frontiers in Emerging Multidisciplinary Sciences

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Decentralized Data-Driven System Supporting Confidential Interoperability Across Diverse Cloud Environments

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Dr. Ankit Patel 1
4 Department of Information Technology, Gujarat Technological University, India,

Abstract

The increasing reliance on distributed cloud infrastructures has necessitated the development of decentralized data-driven systems capable of ensuring secure interoperability across heterogeneous environments. Traditional centralized architectures face limitations in scalability, fault tolerance, and data confidentiality, particularly in multi-cloud ecosystems where data is distributed across diverse platforms. This paper proposes a decentralized data-driven system designed to support confidential interoperability across varied cloud environments while maintaining data integrity, system resilience, and adaptive intelligence.

The proposed framework integrates multi-agent system theory, distributed fault detection mechanisms, and advanced control strategies to enable decentralized coordination among cloud nodes. By leveraging predictive control, reinforcement learning, and distributed optimization techniques, the system facilitates real-time data processing and decision-making without requiring centralized control. Privacy-preserving mechanisms, including secure aggregation and localized data processing, are embedded within the architecture to ensure compliance with data protection requirements.

A comprehensive synthesis of existing literature reveals that while significant advancements have been made in distributed control systems, fault detection, and cloud-based multi-agent coordination, there remains a lack of unified frameworks that address both interoperability and confidentiality. Studies on distributed fault detection (Liang et al., 2022; Wang et al., 2021) and decentralized control (Liu et al., 2023) provide foundational insights into system reliability, while research on predictive and adaptive control highlights the importance of intelligent system behavior (Elsisi et al., 2021; Xu, 2020). Additionally, federated AI approaches demonstrate the potential of decentralized intelligence in multi-cloud environments (Venkiteela & Kesarpu, 2025).

The findings indicate that the proposed system enhances scalability, improves fault tolerance, and ensures secure interoperability across cloud platforms. The integration of decentralized intelligence reduces communication overhead and enhances system adaptability. However, challenges related to synchronization, communication latency, and system heterogeneity remain critical considerations.

This research contributes to the advancement of distributed cloud computing by providing a comprehensive, secure, and scalable framework for decentralized data-driven systems. It establishes a foundation for future research in autonomous cloud systems, intelligent coordination, and privacy-preserving distributed computing.

How to Cite

Dr. Ankit Patel. (2026). Decentralized Data-Driven System Supporting Confidential Interoperability Across Diverse Cloud Environments. Frontiers in Emerging Multidisciplinary Sciences, 3(3), 23–28. Retrieved from https://irjernet.com/index.php/fems/article/view/347
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