Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE 10th International Conference on Big Data Computing Service and Applications, BigDataService 2024
DOI
10.1109/BigDataService62917.2024.00016
First Page
70
Last Page
77
Abstract
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.
Funding Number
RINGS-2148353
Funding Sponsor
National Science Foundation
Keywords
decentralized systems, federated learning, traffic flow prediction
Department
Computer Engineering
Recommended Citation
Andrew Selvia, Ankur Singh, and Wencen Wu. "Vehicular Traffic Flow Prediction via Decentralized Federated Meta-Learning" Proceedings - IEEE 10th International Conference on Big Data Computing Service and Applications, BigDataService 2024 (2024): 70-77. https://doi.org/10.1109/BigDataService62917.2024.00016
Comments
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