mlsanityData to 7 Oct 2026

Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing

arXiv 2407.08462 ↗First posted 11 Jul 2024cs.LG cs.NIcited by 15

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Abstract (arXiv)

Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

26 of 71 references.

References · 26

  1. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  2. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 219
  3. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  4. 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
  5. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  6. 2020Scheduling for Cellular Federated Edge Learning with Importance and Channel Awarenesscited by 62
  7. 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 60
  8. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  9. 2021DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learningcited by 40
  10. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  11. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  12. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
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
  3. 2023Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Studycited by 24
  4. 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
  5. 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
  6. 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
  7. 2021Fast Federated Learning by Balancing Communication Trade-Offscited by 18
  8. 2021Resource Scheduling in Edge Computing: A Surveycited by 17
  9. 2021Communication-Efficient Federated Learning via Quantized Compressed Sensingcited by 17
  10. 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 17
  11. 2018A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communicationscited by 16
  12. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  13. 2020Delay Minimization for Federated Learning Over Wireless Communication Networkscited by 12
  14. 2021Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learningcited by 10

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Citing papers per year: 2024: 8; 2025: 7.

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  1. 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
  2. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  3. 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
  4. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
  5. 2024Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learningcited by 5
  6. 2025FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Datacited by 5
  7. 2025A Survey on Semantic Communications in Internet of Vehiclescited by 3
  8. 2024A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learningcited by 1
  9. 2024Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehiclescited by 0
  10. 2024V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenariocited by 0
  11. 2025Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networkscited by 0
  12. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
3 more
  1. 2025PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2Xcited by 0
  2. 2025Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systemscited by 0
  3. 2025Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusingcited by 0