Split Learning Over Wireless Networks: Parallel Design and Resource Management-Reference-Cited by-同舟云学术

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Split Learning Over Wireless Networks: Parallel Design and Resource Management

Published:2023-04 Issue:4 Volume:41 Page:1051-1066
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Wu Wen¹^ORCID,Li Mushu²^ORCID,Qu Kaige³^ORCID,Zhou Conghao³^ORCID,Shen Xuemin³^ORCID,Zhuang Weihua³^ORCID,Li Xu⁴,Shi Weisen⁴

Affiliation:

1. Frontier Research Center, Peng Cheng Laboratory, Shenzhen, China

2. Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada

3. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada

4. Huawei Technologies Canada Inc., Ottawa, ON, Canada

Funder

Major Key Project of the Peng Cheng Laboratory

National Natural Science Foundation of China

Natural Sciences and Engineering Research Council of Canada

Huawei Technologies Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/49/10075675/10040976.pdf?arnumber=10040976

Reference51 articles.

1. Energy Efficient Federated Learning Over Wireless Communication Networks

2. A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

3. Distributed Learning in Wireless Networks: Recent Progress and Future Challenges

4. Energy-Efficient Federated Edge Learning with Joint Communication and Computation Design

5. Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach

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1. CRSFL: Cluster-based Resource-aware Split Federated Learning for Continuous Authentication;Journal of Network and Computer Applications;2024-11

2. Filling the Missing: Exploring Generative AI for Enhanced Federated Learning Over Heterogeneous Mobile Edge Devices;IEEE Transactions on Mobile Computing;2024-10

3. Efficient Parallel Split Learning Over Resource-Constrained Wireless Edge Networks;IEEE Transactions on Mobile Computing;2024-10

4. ESFL: Efficient Split Federated Learning Over Resource-Constrained Heterogeneous Wireless Devices;IEEE Internet of Things Journal;2024-08-15

5. Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment;ACM Transactions on Sensor Networks;2024-08-10

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