Vehicular Traffic Flow Prediction via Decentralized Federated Meta-Learning

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

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