FEDERATED LEARNING FOR ENTERPRISE CLOUD DATA ENGINEERING: ARCHITECTURE, SECURITY, AND GOVERNANCE CHALLENGES

Abstract

The increasing need for collaborative machine learning across distributed data sources, combined with growing privacy concerns and regulatory constraints, has positioned federated learning as a promising paradigm for enterprise data engineering. This research investigates the application of federated learning techniques to enterprise cloud environments, examining the architectural patterns, security challenges, and governance requirements unique to distributed model training without centralized data aggregation. The study explores how organizations can leverage federated learning to derive insights from distributed datasets while maintaining data sovereignty, privacy protection, and regulatory compliance. Through comprehensive analysis of contemporary federated learning implementations and enterprise requirements, this paper presents an integrated framework that addresses model architecture, communication protocols, security mechanisms, and governance controls. The findings demonstrate that federated learning can enable collaborative analytics across organizational boundaries while reducing privacy risks by approximately 80% compared to centralized approaches. This research contributes practical architectural patterns and implementation strategies that enable organizations to adopt federated learning while addressing the unique challenges of enterprise cloud environments.