AI Driven Federated Healthcare Cloud Architecture for Privacy Preserving Predictive Analytics and Clinical Decision Systems

Abstract

The rapid digitalization of healthcare data presents unprecedented opportunities for predictive analytics and clinical decision support, yet raises critical concerns regarding patient privacy and data security. This study proposes an AI-driven federated healthcare cloud architecture designed to enable collaborative, privacy-preserving predictive analytics and clinical decision systems. Federated learning allows multiple healthcare institutions to collaboratively train AI models on decentralized data without sharing raw patient information, thus addressing key privacy challenges. The architecture integrates advanced AI techniques with secure multi-party computation and encryption mechanisms to ensure data confidentiality and compliance with regulatory standards. By leveraging federated learning within a scalable cloud environment, the system facilitates real-time, accurate predictive analytics and supports clinical decision-making across distributed healthcare networks. The framework improves risk stratification, early disease detection, and personalized treatment recommendations while maintaining strict privacy controls. Experimental evaluation using synthetic and real-world healthcare datasets demonstrates the model’s effectiveness in balancing predictive performance with data privacy. This approach promises to transform healthcare analytics by fostering collaboration without compromising patient confidentiality, thereby enhancing clinical outcomes and operational efficiency. The study underscores the importance of integrating federated AI with cloud infrastructure for future-proof, secure healthcare systems.