FedRFID: Federated Learning for Radio Frequency Fingerprint Identification of WiFi Signals
Jibo Shi, Han Zhang, Sen Wang, Bin Ge, Shiwen Mao, Yun Lin · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
With the rapid development of the cognitive radio networks, the number of terminal devices has exploded. Massive devices generate a large amount of privacy-sensitive data, typically WiFi signals. This paper proposes a method for Radio frequency (RF) fingerprinting identification of WiFi signals based on federated learning, which trains a cooperative model to complete RF fingerprinting identification without transmitting privacy-sensitive data. The experimental findings on a real-world dataset validate that the strategy described in this study increases the RF fingerprinting identification accuracy in a variety of size circumstances, and ensures that data privacy will not be compromised.