ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices-Reference-Cited by-同舟云学术

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ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices

Published:2024-08-15 Issue:16 Volume:11 Page:27153-27166
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhu Guangyu¹^ORCID,Deng Yiqin²^ORCID,Chen Xianhao³^ORCID,Zhang Haixia²^ORCID,Fang Yuguang⁴^ORCID,Wong Tan F.¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA

2. School of Control Science and Engineering and the Shandong Key Laboratory of Wireless Communication Technologies, Shandong University, Jinan, Shandong, China

3. Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China

4. Department of Computer Science, City University of Hong Kong, Hong Kong, China

Funder

National Science Foundation

National Natural Science Foundation of China

HKU IDS Research Seed Fund

Joint Funds of the NSFC

Hong Kong SAR Government under the Global STEM Professorship

Hong Kong Jockey Club under JC STEM Lab of Smart City

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/6488907/10623554/10522472-aam.pdf

Reference33 articles.

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

2. Federated Learning for Internet of Things: A Comprehensive Survey

3. A survey of machine learning for big data processing

4. A survey on federated learning

5. Control of photo sharing over Online Social Networks

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