Federated Learning Architecture for Privacy Preserving Healthcare Data Governance and Scalable Cloud Native Intelligent Systems

Abstract

The rapid digitization of healthcare systems has led to an unprecedented growth in sensitive patient data, raising critical concerns regarding privacy, security, and governance. Traditional centralized machine learning approaches require data aggregation, which increases the risk of data breaches and regulatory non-compliance. This paper proposes a federated learning-based architecture designed to enable privacy-preserving healthcare data governance while supporting scalable intelligent systems within cloud-native platforms. The proposed framework allows distributed healthcare institutions to collaboratively train machine learning models without sharing raw data, thereby ensuring data sovereignty and compliance with regulations such as HIPAA and GDPR. Leveraging cloud-native technologies such as containerization, microservices, and orchestration, the system ensures scalability, resilience, and efficient resource utilization. The architecture integrates secure aggregation protocols, differential privacy mechanisms, and blockchain-based audit trails to enhance trust and transparency. Additionally, it supports real-time analytics and decision-making capabilities critical for modern healthcare applications. Experimental analysis demonstrates improved model accuracy, reduced latency, and enhanced data privacy compared to traditional approaches. This research highlights the potential of federated learning combined with cloud-native infrastructure to transform healthcare data governance into a secure, scalable, and intelligent ecosystem.