Privacy-preserving federated learning for transportation mode prediction based on personal mobility data

Abstract

• We build a federated learning framework for transportation mode prediction based on personal mobility data, which can benefit the public transportation data analysis and resources management. • Our framework can preserve user’s privacy by keeping all user’s personal mobility data in local without uploading to central nodes. • Our framework can train accurate deep neural network models for transportation mode prediction that is close to the centralized training performance. Personal daily mobility trajectories/traces like Google Location Service integrates many valuable information from individuals and could benefit a lot of application scenarios, such as pandemic control and precaution, product recommendation, customized user profile analysis, traffic management in smart cities, etc. However, utilizing such personal mobility data faces many challenges since users’ private information, such as home/work addresses, can be unintentionally leaked. In this work, we build an FL system for transportation mode prediction based on personal mobility data. Utilizing FL-based training scheme, all user’s data are kept in local without uploading to central nodes, providing high privacy preserving capability. At the same time, we could train accurate DNN models that is close to the centralized training performance. The resulted transportation mode prediction system serves as a prototype on user’s traffic mode classification, which could potentially benefit the transportation data analysis and help make wise decisions to manage public transportation resources.