Federated Learning for Digital Twin-Based Vehicular Networks: Architecture and Challenges

by   Latif U. Khan, et al.

Emerging intelligent transportation applications, such as accident reporting, lane change assistance, collision avoidance, and infotainment, will be based on diverse requirements (e.g., latency, reliability, quality of physical experience). To fulfill such requirements, there is a significant need to deploy a digital twin-based intelligent transportation system. Although the twin-based implementation of vehicular networks can offer performance optimization. Modeling twins is a significantly challenging task. Machine learning (ML) can be a preferable solution to model such a virtual model, and specifically federated learning (FL) is a distributed learning scheme that can better preserve privacy compared to centralized ML. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for the twin-based vehicular network. A general architecture showing FL for the twin-based vehicular network is proposed. Our proposed architecture consists of two spaces, such as twin space and a physical space. The physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the paper is concluded and an outlook on open challenges is presented.


page 1

page 2

page 5

page 7


Federated Learning for Vehicular Networks

Machine learning (ML) has already been adopted in vehicular networks for...

Clustered Vehicular Federated Learning: Process and Optimization

Federated Learning (FL) is expected to play a prominent role for privacy...

Federated Learning for Physical Layer Design

Model-free techniques, such as machine learning (ML), have recently attr...

Federated Learning for Computer Vision

Computer Vision (CV) is playing a significant role in transforming socie...

Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

Real-time machine learning has recently attracted significant interest d...

FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy ...

Intelligent Transportation Systems' Orchestration: Lessons Learned Potential Opportunities

The growing deployment efforts of 5G networks globally has led to the ac...

Please sign up or login with your details

Forgot password? Click here to reset