Federated AI Framework for Privacy-Preserving Analytics Across Networked Distributed Enterprise Cloud Environments

Abstract

The increasing adoption of distributed enterprise cloud environments has created significant opportunities for intelligent analytics while simultaneously introducing challenges related to data privacy, security, governance, and regulatory compliance. Conventional centralized artificial intelligence analytics require sensitive organizational data to be transferred to common repositories, increasing exposure to unauthorized access, data leakage, and cross-domain privacy risks. This paper proposes a Federated AI Framework for Privacy-Preserving Analytics Across Networked Distributed Enterprise Cloud Environments, enabling multiple enterprise cloud nodes to collaboratively train intelligent models without directly sharing their underlying data. The proposed framework integrates federated learning, secure aggregation, privacy-preserving mechanisms, adaptive model coordination, cloud-native orchestration, and explainable analytics to support decentralized enterprise intelligence. Local models are trained within participating cloud environments, while only protected model parameters or updates are exchanged through a coordinated federation layer. The methodology incorporates client selection, local preprocessing, distributed model training, encrypted aggregation, privacy evaluation, model validation, and continuous monitoring. The framework is designed to address heterogeneous data distributions, varying computational resources, communication constraints, and organizational governance requirements. The proposed approach provides a scalable architecture for privacy-aware enterprise analytics across interconnected cloud infrastructures while reducing unnecessary movement of sensitive information. The study establishes a methodological foundation for secure, collaborative, and intelligent distributed analytics suitable for modern multi-cloud enterprise environments.