Privacy-Preserving Federated Learning Models for Telehealth: Architecture, Implementation, and Performance Analysis

Abstract

Telehealth systems generate vast amounts of sensitive patient data, requiring secure, scalable, and privacy-preserving computational frameworks. Federated Learning (FL), a decentralized machine-learning paradigm, allows multiple telehealth nodes to collaboratively train predictive models without sharing raw data, thus reducing privacy risks and data transfer burdens. This research investigates privacy-preserving FL models tailored for telehealth applications and evaluates their architectural design, implementation feasibility, and performance efficiency. A multi-site experimental framework involving remote clinics, wearable devices, and hospital servers was designed to assess the impacts of data heterogeneity, communication latency, encryption overhead, and model accuracy. The study incorporates secure aggregation protocols, homomorphic encryption layers, differential privacy (DP) noise calibration, and optimized communication compression to ensure user data confidentiality. Quantitative results show that privacy-preserving FL achieves nearly comparable accuracy to centralized models while significantly improving resilience against data breaches. The architectural and performance analysis demonstrates that an integrated pipeline of FL + DP + secure aggregation offers a robust solution for sensitive telehealth environments such as chronic disease monitoring, medical imaging, and remote diagnostics. The findings contribute to advancing secure digital health infrastructures and provide actionable design guidelines for healthcare institutions and telehealth vendors.