Title: Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach
Authors: Qiqing Wang, Kaidi Yang
Abstract: This paper proposes a privacy-preserving data fusion method for traffic state estimation (TSE). Unlike existing works that assume all data sources to be accessible by a single trusted party, we explicitly address data privacy concerns that arise in the collaboration and data sharing between multiple data owners, such as municipal authorities (MAs) ...
Keywords: Data Fusion; Federated Learning; Data Privacy; Traffic State Estimation; Traffic Flow Theory