A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks

• URL: http://arxiv.org/abs/2501.15288v1
• Date: Sat, 25 Jan 2025 17:57:45 GMT
• Title: A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks
• Abstract summary: jamming attacks pose a significant risk to heterogeneous network (HetNet) architectures.<n>This paper proposes a decentralized two-stage federated learning (FL) framework for jamming detection in 5G femtocells.<n>Our framework achieves a precision of 0.94, recall of 0.90, F1-score of 0.92, and an accuracy of 0.92, while minimizing communication rounds to 30 and achieving robust convergence in detecting jammed signals with an optimal client count of 6.
• Score: 5.715528540446773
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cyber-security for 5G networks is drawing notable attention due to an increase in complex jamming attacks that could target the critical 5G Radio Frequency (RF) domain. These attacks pose a significant risk to heterogeneous network (HetNet) architectures, leading to degradation in network performance. Conventional machine-learning techniques for jamming detection rely on centralized training while increasing the odds of data privacy. To address these challenges, this paper proposes a decentralized two-stage federated learning (FL) framework for jamming detection in 5G femtocells. Our proposed distributed framework encompasses using the Federated Averaging (FedAVG) algorithm to train a Convolutional Autoencoder (CAE) for unsupervised learning. In the second stage, we use a fully connected network (FCN) built on the pre-trained CAE encoder that is trained using Federated Proximal (FedProx) algorithm to perform supervised classification. Our experimental results depict that our proposed framework (FedAVG and FedProx) accomplishes efficient training and prediction across non-IID client datasets without compromising data privacy. Specifically, our framework achieves a precision of 0.94, recall of 0.90, F1-score of 0.92, and an accuracy of 0.92, while minimizing communication rounds to 30 and achieving robust convergence in detecting jammed signals with an optimal client count of 6.

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