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Architecture and Security Analysis of Federated Learning-Based Automatic Modulation Classification

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Frontiers in Cyber Security (FCS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1558))

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Abstract

Conducting effective deep learning for automatic modulation recognition (AMR) plays a key role in non-cooperative communication systems. Applying federated learning (FL) to train AMR-oriented deep neural networks (ADNN) models is promising in achieving high recognition accuracy while maintaining the data privacy and saving precious wireless transmission bandwidth. In this paper, a FL architecture for ADNN training is presented, and four ADNN models are implemented under the FL architecture. Moreover, to evaluate the security vulnerability of the FL architecture facing ADNN models, multiple adversarial attacks are deployed at the clients to attack the central model training. Besides, label poisoning attacks targeting the FL architecture are investigated. To evaluate the efficiency and vulnerability of the FL architecture for ADNN training, a series of experiments are conducted on an open RF dataset. Results show that ADNN models can achieve high recognition accuracy. Moreover, data or label poisoning attacks are more effective in degrading recognition accuracy than adversarial attacks under the FL architecture.

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Author information

Authors and Affiliations

  1. The 63rd Research Institute, National University of Defense Technology, Nanjing, 210007, China

    Xianglin Wei, Qiang Duan & Yongyang Hu

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chao Wang & Xiang Jiao

Authors
  1. Xianglin Wei
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  2. Chao Wang
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  3. Xiang Jiao
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  4. Qiang Duan
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  5. Yongyang Hu
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Corresponding author

Correspondence to Xianglin Wei .

Editor information

Editors and Affiliations

  1. Hainan University, Haikou, China

    Chunjie Cao

  2. National Computer Network Intrusion Protection Center (NCNIPC), Beijing, China

    Yuqing Zhang

  3. Illinois Institute of Technology, Chicago, IL, USA

    Yuan Hong

  4. Nankai University, Tianjin, China

    Ding Wang

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Wei, X., Wang, C., Jiao, X., Duan, Q., Hu, Y. (2022). Architecture and Security Analysis of Federated Learning-Based Automatic Modulation Classification. In: Cao, C., Zhang, Y., Hong, Y., Wang, D. (eds) Frontiers in Cyber Security. FCS 2021. Communications in Computer and Information Science, vol 1558. Springer, Singapore. https://doi.org/10.1007/978-981-19-0523-0_5

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  • DOI: https://doi.org/10.1007/978-981-19-0523-0_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-0522-3

  • Online ISBN: 978-981-19-0523-0

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