Privacy-Preserving Enterprise Analytics Using Federated Learning across Multi-Cloud Infrastructure with Data Governance

Abstract

The increasing distribution of enterprise data across multiple cloud platforms has created significant opportunities for advanced analytics while simultaneously increasing concerns regarding privacy, regulatory compliance, data sovereignty, and centralized data exposure. Conventional machine-learning approaches typically require organizations to aggregate data in a central repository, creating additional security and governance risks when datasets contain confidential, personal, financial, or commercially sensitive information. Federated learning provides an alternative approach by enabling multiple data holders to collaboratively train machine-learning models without directly transferring their raw datasets. This paper proposes a privacy-preserving enterprise analytics framework that combines federated learning, multi-cloud infrastructure, and data governance mechanisms. The proposed framework distributes model training across participating cloud environments while exchanging controlled model updates rather than raw enterprise data. Data governance policies are incorporated into participant selection, model-update transmission, access control, privacy protection, retention, auditing, and model lifecycle management. Additional privacy mechanisms, including secure aggregation, differential privacy, encryption, and identity-based authorization, can be incorporated according to organizational requirements. The research methodology adopts a design-science and experimental approach involving multi-cloud architecture development, federated dataset preparation, model training, privacy-control integration, and comparative evaluation. Performance is assessed through predictive accuracy, convergence time, communication overhead, privacy leakage, governance effectiveness, resource consumption, and model robustness. The proposed framework aims to demonstrate how enterprises can obtain collaborative analytical intelligence while retaining greater control over distributed datasets and strengthening privacy and governance across multi-cloud environments.