Federated Learning for Enterprise Cloud Data Engineering: Architecture, Security, and Governance Challenges

Abstract

This research examines the application of federated learning in enterprise cloud environments, focusing on architecture, security, privacy preservation, and governance challenges. It explores how organizations can collaboratively train machine learning models across distributed datasets without centralizing raw data. The paper presents an enterprise federated learning architecture incorporating model training coordination, secure communication, privacy protection mechanisms, and governance and audit controls. It discusses key security risks, including model poisoning and gradient leakage, along with mitigation strategies such as differential privacy, secure aggregation, participant authentication, and robust model update validation. The study also addresses multi-party federation governance, regulatory compliance, model ownership, privacy-utility trade-offs, operational monitoring, and implementation strategies. It provides guidance for organizations seeking secure, privacy-preserving collaborative analytics across organizational boundaries. Keywords: Federated Learning, Distributed Machine Learning, Privacy-Preserving Analytics, Cloud Data Engineering, Enterprise Security, Data Governance.