Federated Machine Learning for Secure and Personalized Healthcare: A Cloud-Edge Framework for Data-Driven Precision Medicine

Healthcare systems are quickly increasing in using machine learning for precision medicine, management of clinical workflows and to improve patient outcomes. However, the dependence on centrally collected data is problematic in terms of privacy, scalability, and interoperability with real-time clinical decision support. This paper describes a new federated machine learning computing framework that combines cloud-edge computing, health monitoring sensors, and secure aggregation protocols in order to deliver data-driven, personalized healthcare while maintaining patient privacy. The performance of the framework is validated through a pilot simulation on multimodal datasets, showing higher accuracy, lower latency, and resiliency in the face of cyber threats. By integrating concepts of precision medicine, artificial intelligence driven healthcare and wearable technologies, the proposed model fills the innovation vs. clinical adoption gap as we strive for scalable data-driven approaches to care delivery.