LrFedIF: Low-Resource Federated Learning Based on Fingerprint Feature Imitation for Signal Recognition in Non-IID Scenarios
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9021-4_27
Reference15 articles.
1. Lin, Y., Jia, J., Wang, S., Ge, B., Mao, S.: Wireless device identification based on radio frequency fingerprint features. In: ICC 2020-2020 IEEE International Conference on Communications (ICC), pp. 1–6 (2020)
2. Wang, Y., Liu, M., Yang, J., Gui, G.: Data-driven deep learning for automatic modulation recognition in cognitive radios. IEEE Trans. Veh. Technol. 68(4), 4074–4077 (2019)
3. Lin, Y., Tu, Y., Dou, Z., Chen, L., Mao, S.: Contour Stella image and deep learning for signal recognition in the physical layer. IEEE Trans. Cognit. Commun. Netw. 7(1), 34–46 (2020)
4. Lin, Y., Tu, Y., Dou, Z.: An improved neural network pruning technology for automatic modulation classification in edge devices. IEEE Trans. Veh. Technol. 69(5), 5703–5706 (2020)
5. Lin, Y., Zhao, H., Ma, X., Tu, Y., Wang, M.: Adversarial attacks in modulation recognition with convolutional neural networks. IEEE Trans. Reliab. 70(1), 389–401 (2020)
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