Crossmark

A semi-asynchronous federated learning method integrating personalization and staleness awareness for traffic flow prediction in dynamic Internet of Vehicles

Crossref DOI link: https://doi.org/10.1007/s11227-025-07523-0

Published Online: 2025-06-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Zhao, Peng

Liao, Zhuhua

Zhao, Yijiang

Xu, Jianbo

Yi, Aiping
Funding

Funding for this research was provided by:

The Natural Science Foundation of Hunan Province (2024JJ5163, 2024JJ5163)

The Humanities and Social Sciences Project of Ministry of Education of China (24YJAZH237, 24YJAZH237)

The Science and Technology Innovation Program of Hunan Province (2023sk2081)
License Information

Text and Data Mining valid from 2025-06-12

Version of Record valid from 2025-06-12
More Information

Article History

Accepted: 27 May 2025

First Online: 12 June 2025

Declarations

:

: The authors declare no Conflict of interest.

Document is current

Any future updates will be listed below