Sparse Federated Training of Object Detection in the Internet of Vehicles

• URL: http://arxiv.org/abs/2309.03569v1
• Date: Thu, 7 Sep 2023 08:58:41 GMT
• Title: Sparse Federated Training of Object Detection in the Internet of Vehicles
• Authors: Luping Rao, Chuan Ma, Ming Ding, Yuwen Qian, Lu Zhou, Zhe Liu
• Abstract summary: 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.
• Score: 13.864554148921826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an essential component part of the Intelligent Transportation System (ITS), the Internet of Vehicles (IoV) plays a vital role in alleviating traffic issues. Object detection is one of the key technologies in the IoV, which has been widely used to provide traffic management services by analyzing timely and sensitive vehicle-related information. However, the 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, which raises privacy concerns. To mitigate such privacy leakage, we first propose a federated learning-based framework, where well-trained local models are shared in the central server. However, since edge devices usually have limited computing power, plus a strict requirement of low latency in IoVs, we further propose a sparse training process on edge devices, which can effectively lighten the model, and ensure its training efficiency on edge devices, thereby reducing communication overheads. In addition, due to the diverse computing capabilities and dynamic environment, different sparsity rates are applied to edge devices. To further guarantee the performance, we propose, FedWeg, an improved aggregation scheme based on FedAvg, which is designed by the inverse ratio of sparsity rates. Experiments on the real-life dataset using YOLO show that the proposed scheme can achieve the required object detection rate while saving considerable communication costs.

Related papers

• Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks [20.864048794953664]
  This paper proposes a novel framework to address the data privacy issue for Federated Learning (FL)-based Intrusion Detection Systems (IDSs) in Internet-of-Vehicles (IoVs) with limited computational resources. We first propose a highly-effective framework using homomorphic encryption to secure data that requires offloading to a centralized server for processing. We develop an effective training algorithm tailored to handle the challenges of FL-based systems with encrypted data.
  arXiv  Detail & Related papers  (2024-07-26T04:19:37Z)
• FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles [5.803236995616553]
  Federated learning is a promising solution to train sophisticated machine learning models in vehicular networks. We introduce FedPylot, a lightweight MPI-based prototype to simulate federated object detection experiments. Our study factors in accuracy, communication cost, and inference speed, thereby presenting a balanced approach to the challenges faced by autonomous vehicles.
  arXiv  Detail & Related papers  (2024-06-05T20:06:59Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• Edge-assisted U-Shaped Split Federated Learning with Privacy-preserving for Internet of Things [4.68267059122563]
  We present an innovative Edge-assisted U-Shaped Split Federated Learning (EUSFL) framework, which harnesses the high-performance capabilities of edge servers. In this framework, we leverage Federated Learning (FL) to enable data holders to collaboratively train models without sharing their data. We also propose a novel noise mechanism called LabelDP to ensure that data features and labels can securely resist reconstruction attacks.
  arXiv  Detail & Related papers  (2023-11-08T05:14:41Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• 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)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions [0.20236506875465865]
  Intrusion detection systems play a significant role in ensuring security and privacy of smart devices. The present paper aims to present an extensive and exhaustive review on the use of FL in intrusion detection system.
  arXiv  Detail & Related papers  (2021-06-16T13:13:04Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Federated Learning in Vehicular Networks [41.89469856322786]
  Federated learning (FL) framework has been introduced as an efficient tool with the goal of reducing transmission overhead. In this paper, we investigate the usage of FL over centralized learning (CL) in vehicular network applications to develop intelligent transportation systems. We identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management.
  arXiv  Detail & Related papers  (2020-06-02T06:32:59Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.