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In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency.
1912
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PMID: 14741005
D. M. Hawkins, “The problem of overfitting,” Journal of Chemical Information and Computer Sciences · 2004
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Z. Quan, S. Cui, and A. H. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE Journal of Selected Topics in Signal Processing
2008
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S. Hussain and X. Fernando, “Spectrum sensing in cognitive radio networks: Up-to-date techniques and future challenges,” in 2009 IEEE Toronto International Conference Science and Technology for Humanity (TIC-STH)
2009
Cited alongside, same era.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics
2017
Cited alongside, same era.
Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Trans. Intell. Syst. Technol
2019
Cited alongside, same era.
J. Gao, X. Yi, C. Zhong, X. Chen, and Z. Zhang, “Deep learning for spectrum sensing,” IEEE Wireless Communications Letters
2019
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Y. Zhang, Q. Wu, and M. Shikh-Bahaei, “Vertical federated learning based privacy-preserving cooperative sensing in cognitive radio networks,” in 2020 IEEE Globecom Workshops
2020
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G. Zhu, Y. Wang, and K. Huang, “Broadband analog aggregation for low-latency federated edge learning,” IEEE Transactions on Wireless Communications
2020
Later among the works it cites.
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