Node selection for model quality optimization in hierarchical federated learning based on deep reinforcement learning-Reference-Cited by-同舟云学术

Node selection for model quality optimization in hierarchical federated learning based on deep reinforcement learning

Published:2024-03-23 Issue:3 Volume:17 Page:1720-1731
ISSN:1936-6442
Container-title:Peer-to-Peer Networking and Applications
language:en
Short-container-title:Peer-to-Peer Netw. Appl.

Author:

Li Zhuo,Dang Yashi,Chen Xin

Funder

National Natural Science Foundation of China

Beijing Municipal Program for Top Talent

National Key Research and Development Program of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12083-024-01660-8.pdf

Reference41 articles.

1. Reinsel D, Gantz J, Rydning J (2017) IDC white paper, data age 2025

2. Konečnỳ J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon (2016) Federated learning: strategies for improving communication efficiency. arXiv:1610.05492

3. Konečnỳ J, McMahan HB, Ramage D, Richtárik P (2016) Federated optimization: distributed machine learning for on-device intelligence. arXiv:1610.02527

4. Bonawitz K, Eichner H, Grieskamp W, Huba D, Ingerman A, Ivanov V, Kiddon C, Konečnỳ J, Mazzocchi S, McMahan B et al (2019) Towards federated learning at scale: system design. Proc Mach Learn Syst 1:374–388

5. Luo S, Chen X, Wu Q, Zhi Z, Yu S (2020) HFEL: joint edge association and resource allocation for cost-efficient hierarchical federated edge learning. IEEE Trans Wirel Commun 19(10):6535–6548

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Client Selection in Hierarchical Federated Learning;IEEE Internet of Things Journal;2024-09-01