Joint Gradient Sparsification and Device Scheduling for Federated Learning-Reference-Cited by-同舟云学术

Joint Gradient Sparsification and Device Scheduling for Federated Learning

Published:2023-09 Issue:3 Volume:7 Page:1407-1419
ISSN:2473-2400
Container-title:IEEE Transactions on Green Communications and Networking
language:
Short-container-title:IEEE Trans. on Green Commun. Netw.

Author:

Lin Xiaohan¹^ORCID,Liu Yuan¹^ORCID,Chen Fangjiong¹^ORCID,Ge Xiaohu²^ORCID,Huang Yang³^ORCID

Affiliation:

1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China

2. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

3. Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Key Research and Development Program of China

National Nature Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Renewable Energy, Sustainability and the Environment

Link

http://xplorestaging.ieee.org/ielx7/7511293/10224556/10026255.pdf?arnumber=10026255

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4. The convergence of sparsified gradient methods;alistarh;Proc 32nd Conf Neural Inf Process Syst,2018

5. Federated Learning over Wireless Networks: Optimization Model Design and Analysis

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