Peer Review #2 of "OES-Fed: a federated learning framework in vehicular network based on noise data filtering (v0.2)"
2022
Internet of Vehicles (IoV) is an interactive network providing intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control to the running vehicles.One of the main problems in IoV is the reluctance of vehicles to share local data resulting in the cloud server not being able to acquire sufficient amount of data to build accurate Machine Learning (ML) models.Besides, communication efficiency and ML model accuracy in IoV are affected by noise data that being caused by violent shaking, obscuration of in-vehicle cameras.Therefore we propose a new Outlier Detection and Exponential Smoothing federated learning (OES-Fed) framework to overcome the 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 paper achieved higher accuracy, lower loss, and better Area Under Curve (AUC) .The OES-Fed framework we proposed can better filter noise data, providing an important domain reference for starting field of federated learning in loV.