A Novel Federated Learning Framework for IoMT Security Using Integrated Fingerprinting

Abstract

The Internet of Military Things (IoMT) demands robust and secure communication for mission-critical operations. However, traditional cryptographic methods often fall short due to scalability challenges and the resource constraints of IoT devices. To overcome these limitations, we propose a Federated Learning (FL) framework for RF fingerprinting, which utilizes the unique hardware characteristics of devices for authentication, eliminating the need for resource-intensive cryptographic techniques. By enabling distributed learning across devices, FL enhances privacy by minimizing centralized data storage while supporting real-time, continuous authentication. This innovative approach effectively counters spoofing and impersonation attacks, ensuring that only authorized devices can communicate. Simulation results demonstrate that the framework is both scalable and resource-efficient, delivering high authentication accuracy and making it an optimal solution for securing IoMT in dynamic military environments. © 2025 Elsevier B.V., All rights reserved.