Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems-Reference-Cited by-同舟云学术

Joint Air-Ground Distributed Federated Learning for Intelligent Transportation Systems

Published:2023-09 Issue:9 Volume:24 Page:9996-10011
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Shinde Swapnil Sadashiv¹^ORCID,Tarchi Daniele¹^ORCID

Affiliation:

1. Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi,”, University of Bologna, Bologna, Italy

Funder

European Union through the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future,”

“RESTART.”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10235283/10101681.pdf?arnumber=10101681

Reference43 articles.

1. Toward Energy-Efficient Distributed Federated Learning for 6G Networks

2. FedCPF: An Efficient-Communication Federated Learning Approach for Vehicular Edge Computing in 6G Communication Networks

3. Towards a Novel Air–Ground Intelligent Platform for Vehicular Networks: Technologies, Scenarios, and Challenges

4. Aerial Access Networks for Federated Learning: Applications and Challenges

5. Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues

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