Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions-Reference-Cited by-同舟云学术

Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions

Published:2023-12 Issue:4 Volume:18 Page:75-84
ISSN:1556-6072
Container-title:IEEE Vehicular Technology Magazine
language:
Short-container-title:IEEE Veh. Technol. Mag.

Author:

Zhang Xinran¹^ORCID,Liu Jingyuan¹,Hu Tao¹,Chang Zheng²^ORCID,Zhang Yanru³^ORCID,Min Geyong⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), China

3. Shenzhen Institute for Advanced Study and School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China

4. Department of Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, U.K.

Funder

National Natural Science Foundation of China

Sichuan Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/10209/10375848/10214588.pdf?arnumber=10214588

Reference15 articles.

1. BEGIN: Big Data Enabled Energy-Efficient Vehicular Edge Computing

2. Mobile Edge Computing for the Internet of Vehicles: Offloading Framework and Job Scheduling

3. Vehicular Edge Computing and Networking: A Survey

4. Communication-efficient learning of deep networks from decentralized data;McMahan,2017

5. Federated Learning in Vehicular Networks: Opportunities and Solutions

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1. Joint Accuracy and Latency Optimization for Quantized Federated Learning in Vehicular Networks;IEEE Internet of Things Journal;2024-09-01

2. D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing;Applied Sciences;2024-06-07