Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach

• Published in: IEEE internet of things journal Vol. 7; no. 8; pp. 7751 - 7763
• Main Authors: Liu, Yi, Yu, James J. Q., Kang, Jiawen, Niyato, Dusit, Zhang, Shuyu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Agglomeration; Algorithms; Clustering; Clustering algorithms; Computational modeling; Constraint modelling; Data models; Data privacy; Datasets; Deep learning; Federated learning; federated learning (FL); GRU; Machine learning; Neural networks; Organizations; Prediction algorithms; Privacy; privacy protection; Process parameters; Traffic flow; traffic flow prediction (TFP); Traffic models

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...