Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing-Reference-Cited by-同舟云学术

Optimizing Training Efficiency and Cost of Hierarchical Federated Learning in Heterogeneous Mobile-Edge Cloud Computing

Published:2023-05 Issue:5 Volume:42 Page:1518-1531
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Cui Yangguang¹^ORCID,Cao Kun²^ORCID,Zhou Junlong³^ORCID,Wei Tongquan¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Shanghai Key Laboratory of Trustworthy Computing and the Shanghai Trusted Industry Internet Software Collaborative Innovation Center, East China Normal University, Shanghai, China

2. College of Information Science and Technology, Jinan University, Guangzhou, China

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

Funder

NSFC

Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing

China Postdoctoral Science Foundation

Science and Technology Project of Guangzhou

Fundamental Research Funds for the Central Universities

NSF of Jiangsu Province

Open Research Fund of the State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/43/10106059/09882092.pdf?arnumber=9882092

Reference40 articles.

1. Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges

2. Device Scheduling with Fast Convergence for Wireless Federated Learning

3. Adaptive Federated Learning in Resource Constrained Edge Computing Systems

4. Energy Efficient Federated Learning Over Wireless Communication Networks

5. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge

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