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July 8, 2026International Journal of Innovative Computing and Applications

Hierarchical Asynchronous Federated Learning Method for Blockchain-enabled Vehicle Networking Integrating Spatio-temporal Trajectory Characteristics

Li Ting Zhang Yunfei Wang Hou Fu Zhang Ziyang Chen Zeng Sheng

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Key Points

The research aims to develop a hierarchical asynchronous federated learning method to optimize vehicle networking using spatio-temporal trajectory data.
Developed a hierarchical federated learning framework for vehicle networking.
Utilized blockchain technology to enhance data security and collaboration.
Implemented spatio-temporal trajectory analysis for data integration.
Achieved improved data processing speed in vehicle networking systems.
Enhanced collaboration efficiency among vehicles using hierarchical learning.
Provided insights into the integration of blockchain for secure vehicle communications.

Implication

Demonstrates vehicle networking improvements using hierarchical federated learning, highlighting technological benefits.

Abstract

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a508bde6eeac72a437a0418 https://doi.org/10.1504/ijica.2027.10079755

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