OES-Fed: a federated learning framework in vehicular network based on noise data filtering

【Author】 Lei, Yuan; Wang, Shir Li; Su, Caiyu; Ng, Theam Foo

【Source】PEERJ COMPUTER SCIENCE

【影响因子】2.411

【Abstract】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.

【Keywords】Federated learning; Internet of vehicles; Noise data filtering; Outlier detection; Kalman filter; Exponential smoothing

【发表时间】2022 SEP 20

【收录时间】2022-10-24

【文献类型】实验仿真

【主题类别】

区块链技术-协同技术-联邦学习