用联邦学习检测5G射频干扰，不传原始数据也能达97%准确率
Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection
- 在射频域用联邦学习训练模型，各用户设备本地训练不传原始信号
- 97%准确率和F1分数，优于传统集中式方法
- 适合注重隐私的5G网络安全部署
干扰攻击正日益泛滥，严重威胁5G及未来网络的安全。这类攻击针对5G射频（RF）域，可中断无线通信。尽管传统机器学习与深度学习在干扰检测方面展现出潜力，但通常需要集中式数据收集，损害用户设备（UE）隐私。本文提出一种基于联邦学习（FL）的干扰检测框架，直接在射频域从同步信号块（SSBs）提取的空中传输同相与正交（IQ）样本上运行。该框架实现多用户设备间的协作模型训练，无需共享原始射频信号数据。采用联邦平均（FedAvg）算法训练一维卷积神经网络（1DCNN），以有效检测攻击。数值结果表明，所提联邦学习框架达到97%准确率和97% F1分数，优于包括MLP、1DCNN、SVM和逻辑回归在内的集中式基线方法，同时保护所有参与设备的数据隐私。
原文摘要 · Abstract (English)
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
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