  As the most significant data source in smart mobility systems, GPS
trajectories can help identify user travel mode. However, these GPS datasets
may contain users' private information (e.g., home location), preventing many
users from sharing their private information with a third party. Hence,
identifying travel modes while protecting users' privacy is a significant
issue. To address this challenge, we use federated learning (FL), a
privacy-preserving machine learning technique that aims at collaboratively
training a robust global model by accessing users' locally trained models but
not their raw data. Specifically, we designed a novel ensemble-based Federated
Deep Neural Network (eFedDNN). The ensemble method combines the outputs of the
different models learned via FL by the users and shows an accuracy that
surpasses comparable models reported in the literature. Extensive experimental
studies on a real-world open-access dataset from Montreal demonstrate that the
proposed inference model can achieve accurate identification of users' mode of
travel without compromising privacy.
