mlsanityData to 7 Oct 2026

Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

arXiv 2208.01219 ↗First posted 2 Aug 2022cs.DC cs.LGcited by 28

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Abstract (arXiv)

The vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. In VEC, owing to the high-mobility characteristics of vehicles, it is necessary to cache the user data in advance and learn the most popular and interesting contents for vehicular users. Since user data usually contains privacy information, users are reluctant to share their data with others. To solve this problem, traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. However, vehicles may frequently drive out of the coverage area of the VEC before they achieve their local model trainings and thus the local models cannot be uploaded as expected, which would reduce the accuracy of the global model. In addition, the caching capacity of the local RSU is limited and the popular contents are diverse, thus the size of the predicted popular contents usually exceeds the cache capacity of the local RSU. Hence, the VEC should cache the predicted popular contents in different RSUs while considering the content transmission delay. In this paper, we consider the mobility of vehicles and propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR). We first consider the mobility of vehicles and propose an asynchronous FL algorithm to obtain an accurate global model, and then propose an algorithm to predict the popular contents based on the global model. In addition, we consider the mobility of vehicles and propose a deep reinforcement learning algorithm to obtain the optimal cooperative caching location for the predicted popular contents in order to optimize the content transmission delay. Extensive experimental results have demonstrated that the CAFR scheme outperforms other baseline caching schemes.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 2 Aug 2022.

Builds on

15 of 61 references.

References · 15

  1. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  2. 2019Asynchronous Federated Optimizationcited by 264
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2020FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoringcited by 61
  5. 2022Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingcited by 54
  6. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
  7. 2022Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verificationcited by 14
  8. 2020Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communicationscited by 13
  9. 2019Vehicular Edge Computing and Networking: A Surveycited by 10
  10. 2021Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learningcited by 10
  11. 2021Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networkscited by 10
  12. 2016Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networkscited by 5
3 more
  1. 2020Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networkscited by 5
  2. 2021Velocity-adaptive Access Scheme for MEC-assisted Platooning Networks: Access Fairness Via Data Freshnesscited by 4
  3. 2018Towards Mobility-Aware Proactive Caching for Vehicular Ad hoc Networkscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 18; 2025: 5; 2026: 1.

28 papers.

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  1. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  2. 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 17
  3. 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
  4. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  5. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
  6. 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
  7. 2024Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learningcited by 11
  8. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
  9. 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
  10. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  11. 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
  12. 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
16 more
  1. 2023Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computingcited by 5
  2. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
  3. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  4. 2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
  5. 2024Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocationcited by 3
  6. 2025A Survey on Semantic Communications in Internet of Vehiclescited by 3
  7. 2023FedNC: A Secure and Efficient Federated Learning Method with Network Codingcited by 2
  8. 2024Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learningcited by 1
  9. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  10. 2025Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPMcited by 1
  11. 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehiclescited by 0
  12. 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenariocited by 0
  13. 2025Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networkscited by 0
  14. 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2Xcited by 0
  15. 2025Personalized Federated Distillation Assisted Vehicle Edge Caching Strategycited by 0
  16. 2026LLM-Empowered Cooperative Content Caching in Vehicular Fog Caching-Assisted Platoon Networkscited by 0