Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems-Reference-Cited by-同舟云学术

Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems

Published:2023-12 Issue:12 Volume:71 Page:7008-7022
ISSN:0090-6778
Container-title:IEEE Transactions on Communications
language:
Short-container-title:IEEE Trans. Commun.

Author:

Feng Chenyuan¹^ORCID,Yang Howard H.²^ORCID,Wang Siye³^ORCID,Zhao Zhongyuan⁴^ORCID,Quek Tony Q. S.⁵^ORCID

Affiliation:

1. Guangdong–Hong Kong Joint Laboratory for Big Data Imaging and Communication and the Shenzhen Key Laboratory of Digital Creative Technology, Shenzhen University, Shenzhen, China

2. Zhejiang University/University of Illinois at Urbana–Champaign Institute, Zhejiang University, Haining, China

3. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China

4. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

5. Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Tampines, Singapore

Funder

National Natural Science Foundation

Beijing Natural Science Foundation

5G Evolution Wireless Air interface Intelligent R & D and Verification Public Platform Project

National Research Foundation, Singapore and Infocomm Media Development Authority under its Future Communications Research & Development Programme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/26/10364892/10236519.pdf?arnumber=10236519

Reference41 articles.

1. Optimal Design of Hybrid Federated and Centralized Learning in the Mobile Edge Computing Systems

2. Big data caching for networking: moving from cloud to edge

3. Mobile Edge Computing: A Survey on Architecture and Computation Offloading

4. 6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

5. Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

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