Transforming Distributed Enterprise Analytics with Privacy Preserving Federated AI across Multi Cloud Environments

Abstract

The rapid adoption of multi-cloud computing has enabled enterprises to distribute data, applications, and analytical workloads across heterogeneous cloud platforms, geographical regions, and organizational boundaries. Although this architecture improves scalability, resilience, and flexibility, it creates significant challenges for centralized artificial intelligence because sensitive enterprise data cannot always be transferred to a common analytical repository. Privacy regulations, contractual restrictions, intellectual-property concerns, data sovereignty requirements, and security risks further limit conventional data-sharing approaches. Privacy-preserving federated artificial intelligence offers an alternative by enabling multiple enterprise data locations to collaboratively train machine-learning models without directly exchanging their underlying datasets. This research proposes a federated AI framework for distributed enterprise analytics across multi-cloud environments. The framework combines federated learning with privacy-enhancing technologies, secure aggregation, differential privacy, encryption, identity management, and adaptive model orchestration. Local participants train models using their own data, while only protected model updates are communicated to a federated coordination layer. The proposed methodology addresses heterogeneous data distributions, communication efficiency, participant reliability, privacy risks, model convergence, and cross-cloud interoperability. Experimental evaluation is designed to compare federated learning with centralized and conventional distributed-learning approaches using predictive accuracy, communication overhead, convergence time, privacy protection, computational cost, and robustness against adversarial participants. The proposed approach aims to demonstrate how enterprises can obtain collective analytical intelligence while retaining greater control over sensitive data and reducing the risks associated with centralized data aggregation