Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection
For 5G network security, this work provides a privacy-preserving jamming detection method that achieves high accuracy, though it is an incremental application of existing FL techniques to a specific domain.
The paper proposes a federated learning framework for RF jamming detection in 5G networks, achieving 97% accuracy and F1-score, outperforming centralized baselines while preserving data privacy.
Jamming attacks are proliferating and pose a significant threat to the security of 5G and beyond networks. These attacks target 5G radio frequency (RF) domain and can disrupt the communication in wireless networks. While conventional machine learning and deep learning approaches demonstrate its potential for jamming detection, they typically require centralized data collection, compromising the privacy of user equipment (UEs). This work proposes a federated learning (FL)-based jamming detection framework that operates on over-the-air In-phase and Quadrature (IQ) samples extracted from Synchronization Signal Blocks (SSBs) in the RF domain. The framework enables collaborative model training across multiple UEs without sharing raw RF signal data. We adopt Federated Averaging (FedAvg) algorithm to train a 1D convolutional neural network (1DCNN) for effective detection of attacks. Numerical results demonstrate that the proposed FL framework achieves 97% accuracy and 97% F1-score, outperforming centralized baselines including MLP, 1DCNN, SVM, and logistic regression, while preserving the data privacy of all participating UEs