Radio Frequency Fingerprint Identification of WiFi Signals Based on Federated Learning for Different Data Distribution Scenarios

Abstract

The number of terminal devices has skyrocketed along with the quick growth of cognitive radio networks. Massive equipment produce a lot of data that should not be shared, often WiFi signals. The radio frequency (RF) fingerprint identification approach for WiFi signals proposed in this research is based on federated learning and trains a collaborative model to complete RF fingerprint without transferring privacy-sensitive data. Aiming at the lack of labeled data and heterogeneous distribution of labeled data in actual situations, a federated transfer learning mechanism is designed. The technique suggested in this paper increases the accuracy of RF fingerprint at various sizes and assures that data privacy is not compromised, according to experimental results on real-world datasets.