mlsanityData to 13 Oct 2026

Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles

arXiv 2210.15496 ↗First posted 27 Oct 2022eess.SY cs.LG cs.NIcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 26 of its 58 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

26 of 58 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,653
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,328
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,981
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,227
  5. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 635
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  8. 2019Federated Learning over Wireless Fading Channelscited by 182
  9. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  10. 2020Fast-Convergent Federated Learningcited by 61
  11. 2021Anarchic Federated Learningcited by 42
  12. 2020Federated Learning via Intelligent Reflecting Surfacecited by 38
14 more
  1. 2019Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forwardcited by 33
  2. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  3. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  4. 2021Federated Learning over Energy Harvesting Wireless Networkscited by 17
  5. 2022Clustered Vehicular Federated Learning: Process and Optimizationcited by 15
  6. 2022An Overview of 5G Advanced Evolution in 3GPP Release 18cited by 14
  7. 2021FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computingcited by 11
  8. 2020Communication Efficient Federated Learning with Energy Awareness over Wireless Networkscited by 7
  9. 2020Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MECcited by 6
  10. 2021Semi-asynchronous Hierarchical Federated Learning for Cooperative Intelligent Transportation Systemscited by 6
  11. 2022Mobility, Communication and Computation Aware Federated Learning for Internet of Vehiclescited by 5
  12. 2022Efficient Content Delivery in User-Centric and Cache-Enabled Vehicular Edge Networks with Deadline-Constrained Heterogeneous Demandscited by 3
  13. 2019A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimizationcited by 2
  14. 2022Energy Efficiency and Delay Tradeoff in an MEC-Enabled Mobile IoT Networkcited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 3; 2026: 1.

15 papers.

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  1. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  2. 2024Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networkscited by 6
  3. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
  4. 2024Adaptive and Parallel Split Federated Learning in Vehicular Edge Computingcited by 3
  5. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  6. 2024Federated Learning with Heterogeneous Data Handling for Robust Vehicular Object Detectioncited by 2
  7. 2023Resource-Aware Hierarchical Federated Learning for Video Caching in Wireless Networkscited by 1
  8. 2024Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrivalcited by 1
  9. 2025Distribution-Aware Mobility-Assisted Decentralized Federated Learningcited by 1
  10. 2025Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detectioncited by 1
  11. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  12. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0
3 more
  1. 2024Personalized Hierarchical Split Federated Learning in Wireless Networkscited by 0
  2. 2025Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehiclescited by 0
  3. 2026Fed-DLoRA: Efficient Wireless Federated Learning with Dynamic Low-Rank Adaptationcited by 0