Sparse Federated Training of Object Detection in the Internet of Vehicles

TL;DR

This paper introduces a federated learning framework with sparse training for object detection in the Internet of Vehicles, reducing communication costs and accommodating diverse device capabilities while maintaining detection performance.

Contribution

It proposes a novel sparse federated training method with an improved aggregation scheme tailored for IoV edge devices, enhancing privacy, efficiency, and adaptability.

Findings

Achieves effective object detection with reduced communication overhead.

Maintains detection accuracy across diverse device capabilities.

Demonstrates efficiency improvements on real-life datasets.

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.