High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks

TL;DR

This paper proposes a vehicle selection scheme for federated edge learning in vehicular networks that maximizes learning accuracy and maintains cache queue stability by considering vehicle resource statuses and channel conditions.

Contribution

It introduces a novel vehicle selection method that balances data upload stability and learning accuracy in federated edge learning for vehicular networks.

Findings

The scheme outperforms benchmark methods in simulations.

It effectively maintains cache queue stability.

It improves overall learning accuracy.

Abstract

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original data. However, the cache queue in the edge is limited and the channel between edge server and each vehicle is time-varying. Thus, it is challenging to select a suitable number of vehicles to ensure that the uploaded data can keep a stable cache queue in edge server while maximizing the learning accuracy. Moreover, selecting vehicles with different resource statuses to update data will affect the total amount of data involved in training, which further affects the model accuracy. In this paper, we propose a vehicle selection scheme, which maximizes the learning accuracy while ensuring the stability of the cache queue, where the statuses of all the vehicles in the coverage of edge server are taken into account. The performance of this scheme is evaluated through simulation experiments, which indicates that our proposed scheme can perform better than the known benchmark scheme.