Robust federated learning approach for travel mode identification from non-IID GPS trajectories

GPS trajectory is one of the most significant data sources in intelligent transportation systems (ITS). A simple application is to use these data sources to help companies or organizations identify users' travel behavior. However, since GPS trajectory is directly related to private data (e.g., location) of users, citizens are unwilling to share their private information with the third-party. How to identify travel modes while protecting the privacy of users is a significant issue. Fortunately, Federated Learning (FL) framework can achieve privacy-preserving deep learning by allowing users to keep GPS data locally instead of sharing data. In this paper, we propose a Roust Federated Learning-based Travel Mode Identification System to identify travel mode without compromising privacy. Specifically, we design an attention augmented model architectures and leverage robust FL to achieve privacy-preserving travel mode identification without accessing raw GPS data from the users. Compared to existing models, we are able to achieve more accurate identification results than the centralized model. Furthermore, considering the problem of non-Independent and Identically Distributed (non-IID) GPS data in the realworld, we develop a secure data sharing strategy to adjust the distribution of local data for each user, thereby the proposed model with non-IID data can achieve accuracy close to the distribution of IID data. Extensive experimental studies on a real-world dataset demonstrate that the proposed model can achieve accurate identification without compromising privacy and being robust to real-world non-IID data.