Distributed Short-Term Traffic Flow Prediction Based on Integrating Federated Learning and TCN

Abstract

Existing short-term traffic flow prediction methods are based on centralized learning frameworks, which do not consider the diversity of data, leading to limited predictive performance. Additionally, these methods face challenges related to computational efficiency and data privacy. This paper proposes a distributed short-term traffic flow prediction method (PFL-GTCN) that integrates federated learning and TCN. In a distributed framework, multiple edge computing nodes collaborate to complete model training. First, the spectral clustering algorithm divides the spatial graph of the traffic network into multiple subgraphs with similar traffic characteristics. At the same time, G-TCN(Graph-Temporal Convolutional Network) networks are deployed on the edge computing nodes to capture the complex spatiotemporal dependencies within the traffic network. Finally, a personalized federated learning (PFL, Federated Learning with Dynamic Parameter Weights) approach with dynamic weight parameters is introduced, which dynamically adjusts the aggregation weight parameters based on the contribution of the clients. This protects data privacy and reduces communication costs. Experiments conducted on real datasets demonstrate that the proposed prediction method outperforms single models and other graph convolution networks by 1.97%-11.11%. The PFL-GTCN method outperforms traditional centralized training methods by 0.23%-11.22%.

Keywords

• Short-term traffic flow forecast
• personalized federated learning
• distributed