Resources-Efficient Adaptive Federated Learning for Digital Twin-Enabled IIoT-Reference-Cited by-同舟云学术

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Resources-Efficient Adaptive Federated Learning for Digital Twin-Enabled IIoT

Published:2024-07 Issue:4 Volume:11 Page:3639-3652
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Qiao Dewen¹^ORCID,Li Mingyan¹^ORCID,Guo Songtao¹^ORCID,Zhao Jun²^ORCID,Xiao Bin³^ORCID

Affiliation:

1. Key Laboratory of Dependable Service Computing in Cyber-Physical-Society, Ministry of Education, Chongqing University, Chongqing, China

2. School of Computer Science and Engineering, Nanyang Technological University, Singapore

3. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Natural Science Foundation of Chongqing

Special Research Funding for Chongqing Postdoctoral Researchers

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10554364/10490257.pdf?arnumber=10490257

Reference37 articles.

1. Guest Editorial: Industrial Internet of Things: Where Are We and What Is Next?

2. Adaptive Federated Learning and Digital Twin for Industrial Internet of Things

3. Federated learning: Strategies for improving communication efficiency;Koneny,2016

4. Accelerating Federated Learning via Parallel Servers: A Theoretically Guaranteed Approach

5. Realizing the Heterogeneity: A Self-Organized Federated Learning Framework for IoT

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