SVQ-VAE: Federated-Learning-Based Semantic-Aware Communication for Vehicular Networks

The integration of semantic communication technology into intelligent vehicular networks represents a promising research direction, as it significantly reduces data transmission volume and spectrum usage, addressing the high demands of transmitting large-scale visual information between vehicles. Ex...

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Bibliographic Details
Published in:IEEE internet of things journal Vol. 12; no. 23; pp. 51289 - 51304
Main Authors: Jin, Zhu, Li, Yundi, Song, Tiecheng, Jia, Wen-Kang, Song, Xiaoqin
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.12.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Communication
Computer architecture
Data models
Data transmission
Decoding
Federated learning
Heterogeneity
Indexes
Intelligent vehicles
Internet of Things
Networks
Parameters
Semantic communication
semantic-aware compression
Semantics
Servers
Tensors
Training
Transmission efficiency
vector quantized variational autoencoder (VAE)
Vectors
Vehicles
vehicular networks
ISSN:2327-4662, 2327-4662
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Summary:The integration of semantic communication technology into intelligent vehicular networks represents a promising research direction, as it significantly reduces data transmission volume and spectrum usage, addressing the high demands of transmitting large-scale visual information between vehicles. Existing studies typically assume that communicating parties share a common database. However, this assumption poses significant privacy risks, particularly in intervehicle scenarios. To address these challenges, we propose a federated-learning-based semantic-aware communication for vehicular networks. In this approach, each intelligent vehicle locally trains a semantic communication model and uploads its model parameters to the edge server for aggregation. To further enhance data transmission efficiency, we introduce a semantic-aware vector quantized variational autoencoder (SVQ-VAE) architecture as the local semantic communication model. This architecture optimizes transmission by selectively compressing and quantizing only the most relevant semantic information for the task. In addition, to address data heterogeneity among vehicles, we propose a hypernetwork-based personalized federated learning (HPFL) scheme. This approach enhances the model's scalability and generalization by training a hypernetwork at the edge server to generate specific weight parameters for each vehicle's semantic communication model. Simulation experiments on the CIFAR-10 and BDD100K datasets demonstrate that our proposed federated-learning-based semantic-aware communication achieves superior task completion rates, semantic transmission efficiency (STE), and transmission delay compared with existing federated semantic communication architectures.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3612435