mlsanityData to 7 Oct 2026

Mobility-Aware Federated Self-supervised Learning in Vehicular Network

arXiv 2408.00256 ↗First posted 1 Aug 2024cs.LG cs.NIcited by 9

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Abstract (arXiv)

Federated Learning (FL) is an advanced distributed machine learning approach, that protects the privacy of each vehicle by allowing the model to be trained on multiple devices simultaneously without the need to upload all data to a road side unit (RSU). This enables FL to handle scenarios with sensitive or widely distributed data. However, in these fields, it is well known that the labeling costs can be a significant expense, and models relying on labels are not suitable for these rapidly evolving fields especially in vehicular networks, or mobile internet of things (MIoT), where new data emerges constantly. To handle this issue, the self-supervised learning paves the way for training without labels. Additionally, for vehicles with high velocity, owing to blurred images, simple aggregation not only impacts the accuracy of the aggregated model but also reduces the convergence speed of FL. This paper proposes a FL algorithm based on image blur level to aggregation, called FLSimCo, which does not require labels and serves as a pre-training stage for self-supervised learning in the vehicular environment. Simulation results demonstrate that the proposed algorithm exhibits fast and stable convergence.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 26 of its 62 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

26 of 62 references.

References · 26

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14 more
  1. 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
  2. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
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Built on by

Cited by year

Citing papers per year: 2024: 6; 2025: 3.

9 papers.

  1. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  2. 2024DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computingcited by 7
  3. 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
  4. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  5. 2025A Survey on Semantic Communications in Internet of Vehiclescited by 3
  6. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  7. 2025Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPMcited by 1
  8. 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehiclescited by 0
  9. 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2Xcited by 0