mlsanityData to 7 Oct 2026

MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles

arXiv 2212.03519 ↗First posted 7 Dec 2022cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage. However, due to the mobility of vehicles, the connections between the BS and ICVs are short-lived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process. In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL. We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under short-lived wireless connections, so as to increase the convergence speed. Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 7 Dec 2022.

Builds on

17 of 24 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2019Argoverse: 3D Tracking and Forecasting with Rich Mapscited by 728
  4. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  5. 2020Learning Lane Graph Representations for Motion Forecastingcited by 380
  6. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  7. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  8. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  9. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  10. 2015Speeding Up Distributed Machine Learning Using Codescited by 76
  11. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  12. 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 34
5 more
  1. 20215G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learningcited by 20
  2. 2021Fast Federated Learning by Balancing Communication Trade-Offscited by 18
  3. 2021Federated mmWave Beam Selection Utilizing LIDAR Datacited by 12
  4. 2022MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networkscited by 5
  5. 2021To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devicescited by 2

Built on by

Cited by year (2026 to 7 Oct)

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

8 papers.

  1. 2024Adaptive Split Learning over Energy-Constrained Wireless Edge Networkscited by 4
  2. 2024Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learningcited by 3
  3. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  4. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  5. 2023VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learningcited by 0
  6. 2024Mobility-Aware Federated Learning: Multi-Armed Bandit Based Selection in Vehicular Networkcited by 0
  7. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  8. 2026Asynchronous Decentralized Federated Learning over Lossy Wireless Links via Reception- and Age-Aware Aggregationcited by 0