Quantum-Assisted Federated Edge Intelligence With Authenticated Secure Aggregation for Wearable Arrhythmia Detection in IoMT
Document Type
Article
Source of Publication
IEEE Internet of Things Journal
Publication Date
8-15-2026
Abstract
Continuous wearable electrocardiogram monitoring has transformed cardiac assessment into an Internet of Medical Things problem. Yet, existing learning frameworks remain fragmented - addressing robustness, scalability, and cryptographic security separately while neglecting cross-layer deployment constraints. This article presents a unified cross-layer architecture that jointly designs lightweight wearable representation learning, edge-assisted variational quantum classification, Byzantine-resilient federated optimization, and standardized postquantum authenticated model exchange within a single IoT framework. The proposed system treats communication limits, latency budgets, and adversarial stability as first-class design variables. We establish nonconvex convergence guarantees under robust aggregation, PAC-style generalization bounds under heterogeneous client distributions, and explicit upper bounds on adversarial attack success growth. Evaluation on MIT-BIH with cross-dataset validation on PTB-XL demonstrates consistent macro-F1 gains over centralized and conventional federated baselines while preserving calibration quality and bounded communication cost. The results substantiate a deployment-aware secure cardiac intelligence architecture for next-generation IoT healthcare systems, supported by controlled comparisons against parameter-matched classical heads and measured cryptographic/system overheads. While the current evaluation is conducted under simulated NISQ and resource-constrained IoMT conditions, the framework is designed to support future validation on physical quantum and wearable-edge platforms.
DOI Link
ISSN
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
13
Issue
16
First Page
38119
Last Page
38128
Disciplines
Computer Sciences | Medicine and Health Sciences
Keywords
Calibration, ECG arrhythmia detection, federated learning, Internet of Medical Things, postquantum cryptography, robust aggregation, variational quantum classifier
Scopus ID
Recommended Citation
Elhoseny, Mohamed; Taher, Fatma; and Hassan, Mohammed K., "Quantum-Assisted Federated Edge Intelligence With Authenticated Secure Aggregation for Wearable Arrhythmia Detection in IoMT" (2026). All Works. 8203.
https://zuscholars.zu.ac.ae/works/8203
Indexed in Scopus
yes
Open Access
no