Joint Participant Selection and Learning Optimization for Federated Learning of Multiple Models in Edge Cloud
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computational Theory and Mathematics,Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11390-023-3074-4.pdf
Reference36 articles.
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2. Ji S X, Jiang W Q, Walid A, Li X. Dynamic sampling and selective masking for communication-efficient federated learning. IEEE Intelligent Systems, 2022, 37(2): 27–34. https://doi.org/10.1109/MIS.2021.3114610.
3. Sattler F, Wiedemann S, Muller K R, Samek W. Robust and communication-efficient federated learning from non-I. I. D. data. IEEE Trans. Neural Networks and Learning Systems, 2019, 31(9): 3400–3413. https://doi.org/10.1109/TNNLS.2019.2944481.
4. Lim W Y B, Luong N C, Hoang D T, Jiao Y T, Liang Y C, Yang Q, Niyato D, Miao C Y. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031–2063. https://doi.org/10.1109/COMST.2020.2986024.
5. Liu L M, Zhang J, Song S H, Letaief K B. Client-edge-cloud hierarchical federated learning. In Proc. the 2020 EEE International Conference on Communications, Jun. 2020. https://doi.org/10.1109/ICC40277.2020.9148862.
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