A Review of Client Selection Methods in Federated Learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11831-023-10011-4.pdf
Reference118 articles.
1. Yang Q, Liu Y, Chen T et al (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol 10(2):1–19. https://doi.org/10.1145/3298981
2. Abdulrahman S, Tout H, Ould-Slimane H et al (2020) A survey on federated learning: The journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J 8(7):5476–5497. https://doi.org/10.1109/JIOT.2020.3030072
3. Jiang JC, Kantarci B, Oktug S et al (2020) Federated learning in smart city sensing: Challenges and opportunities. Sensors 20(21). https://doi.org/10.3390/s20216230, https://www.mdpi.com/1424-8220/20/21/6230
4. Lim WYB, Luong NC, Hoang DT et al (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials 22(3):2031–2063. https://doi.org/10.1109/COMST.2020.2986024
5. Zhan Y, Zhang J, Hong Z et al (2022) A survey of incentive mechanism design for federated learning. IEEE Transactions on Emerging Topics in Computing 10(2):1035–1044. https://doi.org/10.1109/TETC.2021.3063517
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