Federated Learning: Sum Power Constraints Optimization Design
Author:
Funder
fundamental research funds for the central universities
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-021-06210-5.pdf
Reference26 articles.
1. Yang, Q.; Liu, Y.; Chen, T.; Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1 (2019)
2. Tran, N.H.; Bao, W.; Zomaya, A.; Nguyen, M.N.; Hong, C.S.: Federated learning over wireless networks: optimization model design and analysis. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications (IEEE 1387–1395 (2019)
3. Elgabli, A.; Park, J.; Issaid, C.B.; Bennis, M.: Harnessing wireless channels for scalable and privacy-preserving federated learning. IEEE Trans. Commun. (2021)
4. Chen, M.; Yang, Z.; Saad, W.; Yin, C.; Poor, H.V.; Cui, S.: A joint learning and communications framework for federated learning over wireless networks. IEEE Trans. Wireless Commun. 20(1), 269 (2020)
5. Chen, L.; Qin, X.; Wei, G.: A uniform-forcing transceiver design for over-the-air function computation. IEEE Trans. Wireless Commun. Lett. 7(6), 942 (2018)
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