Sparse Federated Training of Object Detection in the Internet of
  Vehicles
Luping Rao Chuan Ma Ming Ding Yuwen Qian Lu Zhou Zhe Liu
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.