Federated Learning Architecture for Privacy-Preserving Rural Telehealth Intelligence Across Multi-State U.S. Healthcare Networks

Abstract

Rural healthcare systems in the United States continue to face persistent challenges related to limited specialist access, fragmented electronic health records, provider shortages, delayed diagnosis, and uneven telehealth infrastructure. Although telehealth has expanded clinical reach across underserved communities, the effective use of distributed patient data for artificial intelligence remains restricted by privacy regulations, institutional data silos, cybersecurity risks, and cross-state governance barriers. This paper proposes a federated learning architecture for privacy-preserving rural telehealth intelligence across multi-state U.S. healthcare networks. The proposed framework enables rural clinics, telehealth providers, hospitals, and state health networks to collaboratively train predictive models without transferring raw patient data to a central server. It integrates local model training, secure aggregation, differential privacy, encrypted communication, audit controls, and clinical decision-support feedback to support privacy-aware intelligence generation. The architecture is designed to improve chronic disease risk prediction, remote patient monitoring, early deterioration detection, patient stratification, and telehealth triage while reducing exposure of sensitive health information. By combining federated learning with privacy-preserving security layers and rural health governance principles, the paper contributes a scalable model for trustworthy, equitable, and interoperable telehealth intelligence in geographically dispersed healthcare environments.