Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks

Code of Federal Regulations, “Citizens Broadband Radio Service,” Title 47, Part 96, 2015

2015

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2019

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2019

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W. M. Lees, A. Wunderlich, P. J. Jeavons, P. D. Hale, and M. R. Souryal, “Deep Learning Classification of 3.5-GHz Band Spectrograms with Applications to Spectrum Sensing,” IEEE Transactions on Cognitive Communications and Networking , vol. 5, no. 2, pp. 224–-236, 2019

2019

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Y. Shi, K. Davaslioglu, Y. E. Sagduyu, W. C. Headley, M. Fowler, and G. Green, “Deep Learning for Signal Classification in Unknown and Dynamic Spectrum Environments,” IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 2019

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T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated Learning: Challenges, Methods, and Future Directions,” IEEE Signal Processing Magazine , vol. 37, no. 3, pp. 50–60, May 2020

2020

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S. Niknam, H. S. Dhillon, and J. H. Reed, “Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges,” IEEE Communications Magazine , vol. 58, no. 6, pp. 46–51, June 2020

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M. Chen, Z. Yang, W. Saad, C. Yin, H. V. Poor, and S. Cui, “A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks,” IEEE Transactions on Wireless Communications , vol. 20, no. 1, pp. 269–283, Jan. 2021

2021

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Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, “Energy Efficient Federated Learning Over Wireless Communication Networks,” IEEE Transactions on Wireless Communications , vol. 20, no. 3, pp. 1935–1949, March 2021

2021

Later among the works it cites.

M. Chen, D. Gunduz, K. Huang, W. Saad, M. Bennis, A. V. Feljan, and H. V. Poor, “Distributed Learning in Wireless Networks: Recent Progress and Future Challenges,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 12, pp. 3579–3605, Dec. 2021

2021