Secure Data Governance Using Federated Learning Across Multi-Cloud Business Environments for Privacy-Preserving Analytics
DOI:
https://doi.org/10.15662/IJARCST.2026.0905001Keywords:
Federated Learning, Multi-Cloud Computing, Secure Data Governance, Privacy-Preserving Analytics, Data Sovereignty, Differential Privacy, Secure Aggregation, Cloud Security, Machine Learning, Enterprise Data GovernanceAbstract
The rapid adoption of multi-cloud computing has enabled organizations to distribute applications, workloads, and data across multiple cloud service providers to improve scalability, flexibility, availability, and operational efficiency. However, decentralized data environments create significant challenges related to privacy, governance, regulatory compliance, data ownership, and secure analytics. Traditional centralized machine learning approaches require sensitive enterprise data to be transferred to a common analytical repository, increasing exposure to unauthorized access and data leakage. Federated Learning (FL) provides an alternative approach by enabling machine learning models to be trained across distributed data sources while keeping raw data within their original environments. This research proposes a secure data governance framework that integrates federated learning with multi-cloud business environments to support privacy-preserving analytics. The framework combines decentralized model training, secure aggregation, identity management, encryption, access control, differential privacy, policy enforcement, and continuous governance monitoring. Enterprise participants collaboratively contribute model updates rather than directly sharing sensitive datasets. The proposed methodology evaluates model performance, communication efficiency, privacy protection, governance compliance, and resilience against potential attacks. Explainable governance mechanisms are incorporated to provide traceability of training activities and policy decisions across participating cloud environments. The framework aims to establish a scalable and trustworthy approach for collaborative analytics while preserving data sovereignty and reducing unnecessary data movement. The research demonstrates the potential of federated learning to strengthen secure data governance across heterogeneous multi-cloud enterprise ecosystems.
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