Federated Distillation Assisted Vehicle Edge Caching Scheme Based on Lightweight DDPM

• URL: http://arxiv.org/abs/2512.09378v1
• Date: Wed, 10 Dec 2025 07:19:32 GMT
• Title: Federated Distillation Assisted Vehicle Edge Caching Scheme Based on Lightweight DDPM
• Authors: Xun Li, Qiong Wu, Pingyi Fan, Kezhi Wang, Wen Chen, Khaled B. Letaief,
• Abstract summary: Vehicle edge caching can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes.<n>Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data.<n>We propose a federated distillation-assisted vehicle edge caching scheme based on lightweight denoising diffusion probabilistic model (LDPM)
• Score: 39.10185886001752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicle edge caching is a promising technology that can significantly reduce the latency for vehicle users (VUs) to access content by pre-caching user-interested content at edge nodes. It is crucial to accurately predict the content that VUs are interested in without exposing their privacy. Traditional federated learning (FL) can protect user privacy by sharing models rather than raw data. However, the training of FL requires frequent model transmission, which can result in significant communication overhead. Additionally, vehicles may leave the road side unit (RSU) coverage area before training is completed, leading to training failures. To address these issues, in this letter, we propose a federated distillation-assisted vehicle edge caching scheme based on lightweight denoising diffusion probabilistic model (LDPM). The simulation results demonstrate that the proposed vehicle edge caching scheme has good robustness to variations in vehicle speed, significantly reducing communication overhead and improving cache hit percentage.

Related papers

• Hiding in Plain Sight: An IoT Traffic Camouflage Framework for Enhanced Privacy [2.0257616108612373]
  Existing single-technique obfuscation methods, such as packet padding, often fall short in dynamic environments like smart homes.<n>This paper introduces a multi-technique obfuscation framework designed to enhance privacy by disrupting traffic analysis.
  arXiv  Detail & Related papers  (2025-01-26T04:33:44Z)
• Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks [24.85135243655983]
  This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges. U-SFL is able to enhance privacy protection by keeping both raw data and labels on the vehicular user (VU) side. To optimize communication efficiency, we introduce a semantic-aware auto-encoder (SAE) that significantly reduces the dimensionality of transmitted data.
  arXiv  Detail & Related papers  (2024-11-11T07:59:13Z)
• Mobility-Aware Federated Self-supervised Learning in Vehicular Network [8.30695698868618]
  Federated Learning (FL) is an advanced distributed machine learning approach.<n>It 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)<n>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.
  arXiv  Detail & Related papers  (2024-08-01T03:28:10Z)
• Sparse Federated Training of Object Detection in the Internet of Vehicles [13.864554148921826]
  Object detection is one of the key technologies in the Internet of Vehicles (IoV) Current object detection methods are mostly based on centralized deep training, that is, the sensitive data obtained by edge devices need to be uploaded to the server. We propose a federated learning-based framework, where well-trained local models are shared in the central server.
  arXiv  Detail & Related papers  (2023-09-07T08:58:41Z)
• Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations [57.20885629270732]
  We consider privacy aspects of wireless federated learning with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server.
  arXiv  Detail & Related papers  (2022-10-05T13:13:35Z)
• Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning [28.564236336847138]
  vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. Traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. We propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR)
  arXiv  Detail & Related papers  (2022-08-02T03:09:08Z)
• Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification [168.67190934250868]
  Federated learning empowered connected autonomous vehicle (FLCAV) has been proposed. FLCAV preserves privacy while reducing communication and annotation costs. It is challenging to determine the network resources and road sensor poses for multi-stage training.
  arXiv  Detail & Related papers  (2022-06-03T23:55:45Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework [56.57225686288006]
  Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
  arXiv  Detail & Related papers  (2020-03-13T23:52:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.