A Federated Adversarial Learning Approach for Robust Spectrum Sensing

被引:0
|
作者
Catak, Ferhat Ozgur [1 ]
Kuzlu, Murat [2 ]
机构
[1] Univ Stavanger, Dept Elect Engn & Comp Sci, Rogaland, Norway
[2] Old Dominion Univ, Dept Engn Technol, Norfolk, VA USA
来源
2024 13TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING, MECO 2024 | 2024年
关键词
Federated learning; adversarial attack; adversarial training; federated adversarial learning; spectrum sensing; MODELS;
D O I
10.1109/MECO62516.2024.10577941
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
This paper proposes a robust federated learning approach against adversarial attacks for the spectrum sensing application. It is the process of detecting and analyzing radio frequency spectrum to identify available frequencies for transmission spectrum sensing in communication systems. The proposed system utilizes federated learning to train a semantic segmentation model for spectrum sensing in environments with radar and wireless communication systems. The adversarial training at the client level is used to enhance model resilience and the methodology for aggregating local model updates into a robust global model. According to the results, adversarial training strengthens federated learning models against these attacks. Empirical validation through case studies demonstrates the robustness of federated learning against adversarial attacks.
引用
收藏
页码:316 / 319
页数:4
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