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FastFlow: Availability Aware Federated Attention-Based Spatial-Temporal GNN for Big Data-Driven Traffic Forecasting

Author Affiliations
Fordham University, Guangzhou University, North South University
Year2024
Citations2

Abstract

The rapid evolution of Intelligent Transportation Systems (ITS) in the Big Data era, propelled by the Internet of Things (IoT), has led to advanced data-driven vehicle traffic forecasting. Graph Neural Networks (GNNs), particularly the Attention-Based Spatial-Temporal Graph Neural Networks (AST-GNN), are promising in traffic forecasting but face limitations in integrating Big Data with privacy-preserving Federated Learning (FL) due to unique data topology processing. This paper intro-duces the Availability Aware Federated Attention-based Spatial-Temporal Graph Neural Network (FastFlow), an innovative framework that enhances ASTGNN by integrating Federated Learning across entities and employing Big Data methodologies. FastFlow's distinctiveness lies in its availability-aware approach, aggregating adjacency matrices for global topology and utilizing a novel communication protocol that prioritizes data availability and correlation among organizations.…
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