Resource Constrained Vehicular Edge Federated Learning With Highly Mobile Connected Vehicles-Reference-Cited by-同舟云学术

Resource Constrained Vehicular Edge Federated Learning With Highly Mobile Connected Vehicles

Published:2023-06 Issue:6 Volume:41 Page:1825-1844
ISSN:0733-8716
Container-title:IEEE Journal on Selected Areas in Communications
language:
Short-container-title:IEEE J. Select. Areas Commun.

Author:

Pervej Md Ferdous¹^ORCID,Jin Richeng²^ORCID,Dai Huaiyu¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

2. Department of Information and Communication Engineering, Zhejiang–Singapore Innovation and AI Joint Research Laboratory, Zhejiang University, Hangzhou, China

Funder

Zhejiang Provincial Natural Science Foundation of China

Ng Teng Fong Charitable Foundation in the form of ZJU-SUTD IDEA Grant

National Key Research and Development Program of China

U.S. National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

https://ieeexplore.ieee.org/ielam/49/10158390/10121038-aam.pdf

Reference60 articles.

1. Two-Layer Federated Learning With Heterogeneous Model Aggregation for 6G Supported Internet of Vehicles

2. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms;xiao;ArXiv 1708 07747,2017

3. FedParking: A Federated Learning Based Parking Space Estimation With Parked Vehicle Assisted Edge Computing

4. Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning

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