mlsanityData to 7 Oct 2026

Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning

arXiv 2406.17470 ↗First posted 25 Jun 2024cs.LG cs.AI cs.DCcited by 3

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Abstract (arXiv)

Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and interconnected nature of vehicular networks presents unique opportunities to harness direct vehicle-to-vehicle (V2V) communications, enhancing VFL training efficiency. In this paper, we formulate a stochastic optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it. The model aggregation requirements of VFL and the limited transmission time due to mobility result in a stepwise objective function, which presents challenges in solving the problem. We thus propose a derivative-based drift-plus-penalty method to convert the long-term stochastic optimization problem to an online mixed integer nonlinear programming (MINLP) problem, and provide a theoretical analysis to bound the performance gap between the online solution and the offline optimal solution. Further analysis of the scheduling priority reduces the original problem into a set of convex optimization problems, which are efficiently solved using the interior-point method. Experimental results demonstrate that compared with the state-of-the-art benchmarks, the proposed algorithm enhances the image classification accuracy on the CIFAR-10 dataset by 4.20% and reduces the average displacement errors on the Argoverse trajectory prediction dataset by 9.82%.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 62 references that are in the map. First posted on arXiv 25 Jun 2024.

Builds on

35 of 62 references.

References · 35

  1. 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
  2. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  3. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  5. 2017Gradient Sparsification for Communication-Efficient Distributed Optimizationcited by 199
  6. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 186
  7. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  8. 2019Federated Learning over Wireless Fading Channelscited by 178
  9. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  10. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  11. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  12. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
23 more
  1. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  2. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  3. 2020Federated Learning in Vehicular Networkscited by 53
  4. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  5. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  6. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
  7. 2019High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learningcited by 32
  8. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 25
  9. 2020Decentralized SGD with Over-the-Air Computationcited by 16
  10. 2022Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehiclescited by 15
  11. 2024Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networkscited by 11
  12. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learningcited by 11
  13. 2020Edge Learning with Timeliness Constraints: Challenges and Solutionscited by 10
  14. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  15. 2022MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehiclescited by 8
  16. 2022Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learningcited by 7
  17. 2023MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Drivingcited by 7
  18. 2022Semi-Synchronous Personalized Federated Learning over Mobile Edge Networkscited by 5
  19. 2022MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networkscited by 5
  20. 2018Performance Analysis of Sensing-Based Semi-Persistent Scheduling in C-V2X Networkscited by 3
  21. 2021V2X in 3GPP Standardization: NR Sidelink in Rel-16 and Beyondcited by 3
  22. 2018Time synchronization in vehicular ad-hoc networks: A survey ontheory and practicecited by 2
  23. 2021Neighbor Discovery for VANET with Gossip Mechanism and Multi-packet Receptioncited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  2. 2025Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsificationcited by 0
  3. 2026VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSIcited by 0