A Federated Learning Scheme for Privacy Preservation in the Internet of Vehicles

Zihao Shen; Nuo Zhang

doi:10.62051/ijcsit.v8n4.02

Authors

Zihao Shen
Nuo Zhang

DOI:

https://doi.org/10.62051/ijcsit.v8n4.02

Keywords:

Internet of Vehicles, Privacy Preservation, Federated Learning

Abstract

With the rapid development of Internet of Vehicles (IoV) technology, the exponential growth of vehicle data has provided core support for intelligent services such as traffic flow prediction and autonomous driving decision-making. However, as vehicle data contains sensitive information including owner identity, driving trajectories, and data collected by onboard sensors, traditional centralized training models face severe challenges regarding data privacy leakage. To address the aforementioned issues, this paper proposes a Federated Learning Scheme for Privacy Preservation in the Internet of Vehicles (FLSPP). Experimental results demonstrate that the proposed scheme can effectively resist privacy security threats, such as inference attacks, while ensuring high-precision model training. It provides a solution that balances privacy and efficiency for data sharing and collaborative learning in IoV environments.

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