Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app16010029

Authors: Fatiha Benabderrahmane, Elhillali Kerkouche, Nardjes Bouchemal

Institutions: University of Jijel, Centre Universitaire de Mila

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896