# Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles

arXiv: https://arxiv.org/abs/2210.15496 · HTML: https://mlsanity.com/paper/2210.15496/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 58 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [Federated Learning via Intelligent Reflecting Surface](https://mlsanity.com/paper/2011.05051/) (2020)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks](https://mlsanity.com/paper/2012.05137/) (2020)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [Federated Learning over Energy Harvesting Wireless Networks](https://mlsanity.com/paper/2106.08809/) (2021)
- [Clustered Vehicular Federated Learning: Process and Optimization](https://mlsanity.com/paper/2201.11271/) (2022)
- [An Overview of 5G Advanced Evolution in 3GPP Release 18](https://mlsanity.com/paper/2201.01358/) (2022)
- [FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing](https://mlsanity.com/paper/2110.12876/) (2021)
- [Communication Efficient Federated Learning with Energy Awareness over Wireless Networks](https://mlsanity.com/paper/2004.07351/) (2020)
- [Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC](https://mlsanity.com/paper/2006.13044/) (2020)
- [Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/2110.09073/) (2021)
- [Mobility, Communication and Computation Aware Federated Learning for Internet of Vehicles](https://mlsanity.com/paper/2205.09529/) (2022)
- [Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demands](https://mlsanity.com/paper/2202.07792/) (2022)
- [A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization](https://mlsanity.com/paper/1902.07014/) (2019)
- [Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Network](https://mlsanity.com/paper/2202.03648/) (2022)

## Built on by (most cited first, 15 of 15)

- [Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneity](https://mlsanity.com/paper/2308.01562/) (2023)
- [Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks](https://mlsanity.com/paper/2411.06773/) (2024)
- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)
- [Adaptive and Parallel Split Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2405.18707/) (2024)
- [Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning](https://mlsanity.com/paper/2406.17470/) (2024)
- [Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detection](https://mlsanity.com/paper/2405.01108/) (2024)
- [Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networks](https://mlsanity.com/paper/2311.06918/) (2023)
- [Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival](https://mlsanity.com/paper/2408.05886/) (2024)
- [Distribution-Aware Mobility-Assisted Decentralized Federated Learning](https://mlsanity.com/paper/2505.18866/) (2025)
- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning](https://mlsanity.com/paper/2311.18741/) (2023)
- [Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Network](https://mlsanity.com/paper/2410.10451/) (2024)
- [Personalized Hierarchical Split Federated Learning in Wireless Networks](https://mlsanity.com/paper/2411.06042/) (2024)
- [Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles](https://mlsanity.com/paper/2506.02972/) (2025)
- [Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptation](https://mlsanity.com/paper/2604.24103/) (2026)

## Abstract (arXiv)

This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles' (CVs') onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs' trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs' dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model.