Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical
  Federated Learning Approach
Qiqing Wang Kaidi Yang
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) and mobility providers (MPs). To
this end, we propose a novel vertical federated learning (FL) approach, FedTSE,
that enables multiple data owners to collaboratively train and apply a TSE
model without having to exchange their private data. To enhance the
applicability of the proposed FedTSE in common TSE scenarios with limited
availability of ground-truth data, we further propose a privacy-preserving
physics-informed FL approach, i.e., FedTSE-PI, that integrates traffic models
into FL. Real-world data validation shows that the proposed methods can protect
privacy while yielding similar accuracy to the oracle method without privacy
considerations.