Vehicular Traffic Flow Prediction via Decentralized Federated Meta-Learning

Traffic flow prediction (TFP) has become a crucial problem for billions of people who rely on traffic predictions to optimize their travels every day. In this paper, we present a decentralized federated meta-learning (DFML) framework for traffic flow prediction. Within this framework, traffic detectors spread across a road system can form federations with nearby peers in order to predict traffic collaboratively. A node that assembles a federation assumes the role of receiver while the remainder are assigned as broadcasters. The broadcasters learn model parameters from local data and then broadcast those models to receivers, thereby enriching the available information from which a receiver may learn. The framework combines the strength of decentralized federated learning and meta-learning frameworks to leverage distributed data sources without central aggregation, while also enabling models to generalize across diverse traffic conditions and environments efficiently. Evaluation of the DFML algorithm, using data from the Caltrans Performance Measurement System (PeMS), demonstrates initial viability for TFP and establishes a foundation for continued innovation.