Federated AI Learning across Cloud Environments for Enterprise Data Privacy and Distributed Intelligence Networks (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2026)
Federated Artificial Intelligence (AI) learning across cloud environments is emerging as an important approach for organizations seeking to develop intelligent systems without centralizing sensitive enterprise data. Traditional machine-learning architectures generally require data to be transferred to a central repository where models are trained, creating concerns related to privacy, security, regulatory compliance, data ownership, and communication overhead. Federated learning addresses these limitations by allowing AI models to be trained across distributed data environments while keeping the original data within its respective location. In an enterprise context, data may be distributed across multiple public clouds, private clouds, edge environments, branch offices, data centers, and organizational departments. Federated AI enables these geographically and technologically distributed environments to collaborate by exchanging model parameters, gradients, or other learned information instead of raw datasets. This research examines a federated AI architecture designed for multi-cloud enterprise environments and focuses on how distributed intelligence can improve privacy while maintaining analytical capabilities. The proposed methodology incorporates cloud-based federated coordination, local model training, secure aggregation, privacy-preserving mechanisms, communication management, model validation, and governance. The architecture supports heterogeneous enterprise data sources while allowing organizations to retain control over their information. The study argues that federated AI can provide a foundation for privacy-aware distributed intelligence networks by combining machine learning with decentralized data management. The proposed approach is particularly relevant to enterprises operating under strict privacy, security, and data-residency requirements, where conventional centralized AI architectures may introduce significant operational and compliance risks
Keywords: Federated Learning; Artificial Intelligence; Enterprise Data Privacy; Multi-Cloud Computing; Distributed Intelligence; Machine Learning; Secure Aggregation; Privacy-Preserving AI; Cloud Computing; Data Governance; Decentralized Learning; Enterprise AI; Differential Privacy; Model Security; Distributed Networks