Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications-Reference-Cited by-同舟云学术

Hierarchical Federated Learning With Social Context Clustering-Based Participant Selection for Internet of Medical Things Applications

Published:2023-08 Issue:4 Volume:10 Page:1742-1751
ISSN:2329-924X
Container-title:IEEE Transactions on Computational Social Systems
language:
Short-container-title:IEEE Trans. Comput. Soc. Syst.

Author:

Zhou Xiaokang¹^ORCID,Ye Xiaozhou²^ORCID,Wang Kevin I-Kai²^ORCID,Liang Wei³^ORCID,Nair Nirmal Kumar C.²^ORCID,Shimizu Shohei¹^ORCID,Yan Zheng⁴^ORCID,Jin Qun⁵^ORCID

Affiliation:

1. Faculty of Data Science, Shiga University, Hikone, Japan

2. Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland, New Zealand

3. Changsha Social Laboratory of Artificial Intelligence, Hunan University of Technology and Business, Changsha, China

4. State Key Laboratory on Integrated Services Networks and School of Cyber Engineering, Xidian University, Xi’an, China

5. Faculty of Human Sciences, Waseda University, Tokorozawa, Japan

Funder

QuakeCoRE, a New Zealand Tertiary Education Commission-funded center

Hunan Provincial Innovation Foundation For Postgraduate

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Human-Computer Interaction,Social Sciences (miscellaneous),Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/6570650/10201450/10091843.pdf?arnumber=10091843

Reference39 articles.

1. PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks

2. FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning

3. Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing

4. Multi-Armed Bandit-Based Client Scheduling for Federated Learning

5. Deep Federated Q-Learning-Based Network Slicing for Industrial IoT

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