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

ArXi:2605.01705v1 Announce Type: cross 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 nstrate its potential for jamming detection, they typically require centralized data collection, compromising the privacy of user equipment (UEs