Risk-Aware Privacy-Preserving Federated Learning for Remote Patient Monitoring: A Multi-Layer Adaptive Security Framework

The integration of artificial intelligence into remote patient monitoring (RPM) offers significant benefits for proactive and continuous healthcare, but also raises critical concerns regarding privacy, integrity, and robustness. Federated Learning (FL) provides a decentralized approach to model training that preserves data locality, yet most existing solutions address only isolated security aspects and lack contextual adaptability for clinical use. This paper presents MedGuard-FL, a context-aware FL framework tailored to e-healthcare environments. Spanning device, edge, and cloud layers, it integrates encryption, adaptive differential privacy, anomaly detection, and Byzantine-resilient aggregation. At its core, a policy engine dynamically adjusts privacy and robustness parameters based on the patient’s status and the system’s risk. Evaluations on real-world clinical datasets show MedGuard-FL maintains high model accuracy while neutralizing various adversarial attacks (e.g., label-flip, poisoning, backdoor, membership inference), all with manageable latency. Compared to static defenses, it offers improved trade-offs between privacy, utility, and responsiveness. Additional edge-level privacy analyses confirm its resilience, with attack effectiveness near random. By embedding clinical risk awareness into adaptive defense mechanisms, MedGuard-FL lays a foundation for secure, real-time federated intelligence in RPM.