Privacy-Preserving Authentication for IoMT: A Federated Learning Approach Leveraging RF Fingerprinting

Abstract

The Internet of Military Things (IoMT) requires secure, reliable communication to support critical operations, but traditional cryptographic methods often struggle with scalability and place significant demands on IoT device resources. To overcome these limitations, we present a Federated Learning (FL) framework for RF fingerprinting that uses distinct hardware characteristics for device authentication, minimizing the need for heavy cryptographic processes. FL facilitates distributed learning across devices, enhancing privacy by reducing data centralization and enabling continuous, real-time authentication. This framework effectively protects against spoofing and impersonation attacks, ensuring secure communication among authorized devices. Simulations demonstrate that the approach is scalable, resource-efficient, and achieves high authentication accuracy, making it ideally suited for securing IoMT in complex military environments. © 2025 IEEE.