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

arXiv: https://arxiv.org/abs/2208.01890 · HTML: https://mlsanity.com/paper/2208.01890/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2022.

## Built on by (most cited first, 26 of 26)

- [Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network](https://mlsanity.com/paper/2401.09886/) (2024)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08458/) (2024)
- [Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2404.08444/) (2024)
- [Mobility-Aware Federated Self-supervised Learning in Vehicular Network](https://mlsanity.com/paper/2408.00256/) (2024)
- [DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV](https://mlsanity.com/paper/2408.09194/) (2024)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing](https://mlsanity.com/paper/2304.02832/) (2023)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)
- [Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2411.04672/) (2024)
- [ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling](https://mlsanity.com/paper/2210.02119/) (2022)
- [Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation](https://mlsanity.com/paper/2407.11310/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.02342/) (2024)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)
- [Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network](https://mlsanity.com/paper/2407.07575/) (2024)
- [Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles](https://mlsanity.com/paper/2409.17287/) (2024)
- [V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario](https://mlsanity.com/paper/2410.22987/) (2024)
- [Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks](https://mlsanity.com/paper/2501.08037/) (2025)
- [PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X](https://mlsanity.com/paper/2501.12656/) (2025)
- [PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing](https://mlsanity.com/paper/2603.09082/) (2026)
- [Joint Optimization of Trajectory Control, Resource Allocation, and Task Offloading for Multi-UAV-Assisted IoV](https://mlsanity.com/paper/2605.04436/) (2026)

## Abstract (arXiv)

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.