Developing Federated AI Frameworks for Distributed Cloud Enterprise Data Governance and Secure Intelligence

Abstract

Modern multi-national enterprises heavily rely on distributed cloud environments to process vast amounts of telemetry, transaction logs, and customer interactions. However, centralized artificial intelligence (AI) training paradigms introduce significant data privacy risks, regulatory compliance hurdles, and prohibitive bandwidth overheads. This paper explores the architectural design and implementation of Federated AI Frameworks tailored for distributed cloud infrastructures to achieve robust enterprise data governance and secure intelligence. By decoupling model training from raw data access, federated learning (FL) enables local enterprise nodes to train AI models on-site and transmit only encrypted gradients or model updates to a global aggregation server. We propose a enterprise-grade federated framework integrating Secure Multi-Party Computation (SMPC), Differential Privacy (\(\epsilon, \delta\)), and blockchain-based audit logging to ensure traceably governance across hybrid and multi-cloud systems. Our framework addresses critical challenges, including system heterogeneity, non-IID (non-independently and identically distributed) data skew, and adversarial parameter poisoning. Experimental evaluations demonstrate that our framework maintains up to 94.2% model accuracy relative to centralized baselines while reducing cross-region egress traffic by over 78% and guaranteeing strict adherence to global privacy mandates like GDPR and HIPAA.