Heterogeneous Semi-Asynchronous Federated Learning in Internet of Things: A Multi-Armed Bandit Approach-Reference-Cited by-同舟云学术

Heterogeneous Semi-Asynchronous Federated Learning in Internet of Things: A Multi-Armed Bandit Approach

Published:2022-10 Issue:5 Volume:6 Page:1113-1124
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Chen Shuai¹,Wang Xiumin¹^ORCID,Zhou Pan²^ORCID,Wu Weiwei³^ORCID,Lin Weiwei¹^ORCID,Wang Zhenyu⁴

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

2. Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

3. School of Computer Science and Engineering, Southeast University, Nanjing, China

4. School of Software Engineering, South China University of Technology, Guangzhou, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Science and Technology Program of Guangzhou

Guangzhou Science and Technology Program key projects

Guangdong Major Project of Basic and Applied Basic Research

Guangzhou Development Zone Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7433297/9900106/09713759.pdf?arnumber=9713759

Reference41 articles.

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2. Asynchronous Multi-task Learning

3. Asynchronous federated optimization;xie,2020

4. Deep gradient compression: Reducing the communication bandwidth for distributed training;lin;Proc Int Conf Learn Representations,0

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