Lei Yuan, Wang Shir Li, Su Caiyu, Ng Theam Foo
Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia.
Guangxi Vocational & Technical Institute of Industry, Nanning, Guangxi, China.
PeerJ Comput Sci. 2022 Sep 20;8:e1101. doi: 10.7717/peerj-cs.1101. eCollection 2022.
The Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to running vehicles. One of the main problems in the IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire a sufficient amount of data to build accurate machine learning (ML) models. In addition, communication efficiency and ML model accuracy in the IoV are affected by noise data caused by violent shaking and obscuration of in-vehicle cameras. Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome these problems. More specifically, we filter the noise data of the local ML model in the IoV from the current perspective and historical perspective. The noise data filtering is implemented by combining data outlier, K-means, Kalman filter and exponential smoothing algorithms. The experimental results of the three datasets show that the OES-Fed framework proposed in this article achieved higher accuracy, lower loss, and better area under the curve (AUC). The OES-Fed framework we propose can better filter noise data, providing an important domain reference for starting field of federated learning in the IoV.
车联网（IoV）是一个交互式网络，为行驶中的车辆提供智能交通管理、智能动态信息服务和智能车辆控制。车联网的主要问题之一是车辆不愿共享本地数据，导致云服务器无法获取足够的数据来构建准确的机器学习（ML）模型。此外，车联网中的通信效率和ML模型准确性会受到车载摄像头剧烈抖动和遮挡所产生的噪声数据的影响。因此，我们提出了一种新的异常检测与指数平滑联邦学习（OES-Fed）框架来克服这些问题。更具体地说，我们从当前视角和历史视角对车联网中本地ML模型的噪声数据进行过滤。噪声数据过滤通过结合数据异常值、K均值、卡尔曼滤波器和指数平滑算法来实现。三个数据集的实验结果表明，本文提出的OES-Fed框架实现了更高的准确率、更低的损失以及更好的曲线下面积（AUC）。我们提出的OES-Fed框架能够更好地过滤噪声数据，为车联网中联邦学习的起始领域提供了重要的领域参考。
