Federated Learning for Privacy preserving Health Monitoring in Multi Hospital Smart Healthcare Systems

Abstract

With the rapid advancement in digital technology, hospitals are becoming “smart” hospitals through the use of connected devices and more advanced analytics to enhance patient care. However, hospitals are subject to significant privacy, security, and compliance concerns when collecting and sharing sensitive and personally identifiable health information with the public due to strict privacy regulations such as HIPAA, GDPR, etc. One approach that might offer some utility to address these issues is “federated learning”. This approach enables hospitals to collaborate on building powerful artificial intelligence models, while never sharing patient data. Instead, each hospital keeps its data safe on-site and only shares updates to the model. In this study, we present a system that uses “federated learning” to enable privacy-focused health monitoring across multiple hospitals. We include techniques to further protect patient data during training and test the system’s performance using simulations that mimic real hospital settings. We also discuss practical challenges and legal considerations for implementing this technology. Our findings show that “federated learning” can help hospitals learn from one another and improve patient care while ensuring privacy and regulatory compliance.