Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network…

• NR
  National Research Foundation Singapore

  Awards: DeSTSCI2019-0007, NRF2015-NRF-ISF001-2277
• EM
  Energy Market Authority of Singapore

  Award: NRF2017EWT-EP003-041