FedRFID: Federated Learning for Radio Frequency Fingerprint Identification of WiFi Signals
Jibo Shi, Han Zhang, Sen Wang, Bin Ge, Shiwen Mao, Yun Lin
Abstract
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.