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This paper investigates the transmission power control in over-the-air federated edge learning (Air-FEEL) system.
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J. Dong, Y. Shi, and Z. Ding, “Blind over-the-air computation and data fusion via provable Wirtinger flow,” IEEE Trans. Signal Process. , vol. 68, pp. 1136–1151, Jan. 2020
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Y. Koda, K. Yamamoto, T. Nishio, and M. Morikura, “Differentially private AirComp federated learning with power adaptation harnessing receiver noise,” in Proc. IEEE GLOBECOM , Dec. 2020, pp. 1–6
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H. Guo, A. Liu, and V. K. N. Lau, “Analog gradient aggregation for federated learning over wireless networks: Customized design and convergence analysis,” IEEE Internet Things J. , vol. 8, no. 1, pp. 197–210, Jan. 2021
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C. Xu, S. Liu, Z. Yang, Y. Huang, and K.-K. Wong, “Learning rate optimization for federated learning exploiting over-the-air computation,” IEEE J. Sel. Areas Commun. , pp. 1–1, 2021
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G. Zhu, Y. Du, D. Gündüz, and K. Huang, “One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis,” IEEE Trans. Wireless Commun. , vol. 20, no. 3, pp. 2120–2135, Mar. 2021
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D. Liu and O. Simeone, “Privacy for free: Wireless federated learning via uncoded transmission with adaptive power control,” IEEE J. Sel. Areas Commun. , vol. 39, no. 1, pp. 170–185, Jan. 2021
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N. Zhang and M. Tao, “Gradient statistics aware power control for over-the-air federated learning,” IEEE Trans. Wireless Commun. , vol. 20, no. 8, pp. 5115–5128, Aug. 2021
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