An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation-Reference-Cited by-同舟云学术

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An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Published:2022 Issue: Volume: Page:1-15
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Xu Chenhao¹^ORCID,Qu Youyang²^ORCID,Luan Tom H.³^ORCID,Eklund Peter W.⁴^ORCID,Xiang Yong¹^ORCID,Gao Longxiang⁵^ORCID

Affiliation:

1. Deakin Blockchain Innovation Lab, School of Information Technology, Deakin University, Geelong, Australia

2. Data 61 Australia Commonwealth Scientific and Industrial Research Organization, Australia

3. School of Cyber Engineering, Xidian University, Shaanxi, China

4. School of Information Technology, Deakin University, Geelong, Australia

5. Qilu University of Technology and Shandong Computer Science Center, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/4356907/10002314.pdf?arnumber=10002314

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1. Responsible Federated Learning in Smart Transportation: Outlooks and Challenges;IEEE Internet of Things Magazine;2024-09

2. Deep Fingerprinting Data Learning Based on Federated Differential Privacy for Resource-Constrained Intelligent IoT Systems;IEEE Internet of Things Journal;2024-08-01

3. VSFL: Trajectory prediction framework based on validity-aware semi-asynchronous federated learning in internet of vehicles;Computer Communications;2024-08

4. Blockchain‐based federated learning approaches in internet of things applications;SECURITY AND PRIVACY;2024-06-18

5. A Learning-Based Hierarchical Edge Data Corruption Detection Framework in Edge Intelligence;IEEE Internet of Things Journal;2024-05-15

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