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Federated learning framework enhances 5G jamming detection with 97% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a federated learning framework to detect RF jamming attacks in 5G networks. This approach trains a 1D convolutional neural network using In-phase and Quadrature samples from Synchronization Signal Blocks, allowing collaborative model training across user equipment without sharing raw signal data. The federated learning method achieved 97% accuracy and F1-score, outperforming centralized machine learning models while preserving user privacy. AI

IMPACT Enhances 5G network security by enabling privacy-preserving, collaborative detection of jamming attacks.

RANK_REASON Academic paper presenting a novel federated learning approach for RF jamming detection in 5G networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Federated learning framework enhances 5G jamming detection with 97% accuracy

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Academic paper presenting a novel federated learning approach for RF jamming detection in 5G networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Samhita Kuili, Mohammadreza Amini, Burak Kantarci ·

    Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection

    arXiv:2605.01705v1 Announce Type: cross Abstract: 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 conventio…

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