  Existing traffic flow forecasting approaches by deep learning models achieve
excellent success based on a large volume of datasets gathered by governments
and organizations. However, these datasets 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 trade-off between these two objectives. To address
this challenge, we introduce a privacy-preserving machine learning technique
named federated learning and propose a Federated Learning-based Gated Recurrent
Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU
differs from current centralized learning methods and updates universal
learning models through a secure parameter aggregation mechanism rather than
directly sharing raw data among organizations. In the secure parameter
aggregation mechanism, we adopt a Federated Averaging algorithm to reduce the
communication overhead during the model parameter transmission process.
Furthermore, we design a Joint Announcement Protocol to improve the scalability
of FedGRU. We also propose an ensemble clustering-based scheme for traffic flow
prediction by grouping the organizations into clusters before applying FedGRU
algorithm. Through extensive case studies on a real-world dataset, it is shown
that FedGRU's prediction accuracy is 90.96% higher than the advanced deep
learning models, which confirm that FedGRU can achieve accurate and timely
traffic prediction without compromising the privacy and security of raw data.
