FedDeepRiskNet: A Federated Learning Framework for Secure and Efficient Multi-Hospital Management

Abstract

Existing multi-hospital management frameworks, especially those that combine vital sign IoT data with elasticity approaches, often have data storage, poor predictive skills, and poor human-centered design. This study tackles these issues and provide safe and private data exchange, by proposing a novel framework that works with a federated learning technique. Federated learning is a decentralized machine learning (ML) method that enables several hospitals to work together on model training without compromising the privacy of their patient data. The proposed approach directly addresses the issue of data storage by integrating the work with a deep learning (DL) algorithm. The aim of this research is to improve resources allocation, improve patient outcomes, and find diseases earlier. The DL method employed is a more sophisticated and more effective method of patient risk categorization. This framework greatly contributes to the development of healthcare 4.0 by allowing more effective, equitable, and patient-centered care across multi-hospital networks by solving the issues of data interoperability, improving prediction accuracy, and placing a priority on user experience.