A Short-Term Traffic Flow Prediction Method Based on Personalized Lightweight Federated Learning

Traffic flow prediction can guide the rational layout of land use. Accurate traffic flow prediction can provide an important basis for urban expansion planning. This paper introduces a personalized lightweight federated learning framework (PLFL) for traffic flow prediction. This framework has been improved and enhanced to better accommodate traffic flow data. It is capable of collaboratively training a unified global traffic flow prediction model without compromising the privacy of individual datasets. Specifically, a spatiotemporal fusion graph convolutional network (MGTGCN) is established as the initial model for federated learning. Subsequently, a shared parameter mechanism of federated learning is employed for model training. Customized weights are allocated to each client model based on their data features to enhance personalization during this process. In order to improve the communication efficiency of federated learning, dynamic model pruning (DMP) is introduced on the client side to reduce the number of parameters that need to be communicated. Finally, the PLFL framework proposed in this paper is experimentally validated using LPR data from Changsha city. The results demonstrate that the framework can still achieve favorable prediction outcomes even when certain clients lack data. Moreover, the communication efficiency of federated learning under this framework has been enhanced while preserving the distinct characteristics of each client, without significant interference from other clients.