Spatial-temporal traffic prediction technology is crucial for network planning, resource allocation optimizing, and user experience improving. With the development of virtual network operators, multi-operator collaborations, and edge computing, spatial-temporal traffic data has taken on a distributed nature. Consequently, non-centralized spatial-temporal traffic prediction solutions have emerged as a recent research focus. Currently, the majority of research typically adopts federated learning methods to train traffic prediction models distributed on each base station. This method reduces additional burden on communication systems. However, this method has a drawback: it cannot handle irregular traffic data. Due to unstable wireless network environments, device failures, insufficient storage resources, etc., data missing inevitably occurs during the process of collecting traffic data. This results in the irregular nature of distributed traffic data. Yet, commonly used traffic prediction models such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) typically assume that the data is complete and regular. To address the challenge of handling irregular traffic data, this paper transforms irregular traffic prediction into problems of estimating latent variables and generating future traffic. To solve the aforementioned problems, this paper introduces split learning to design a structured distributed learning framework. The framework comprises a Global-level Spatial structure mining Model (GSM) and several Node-level Generative Models (NGMs). NGM and GSM represent Seq2Seq models deployed on the base station and graph neural network models deployed on the cloud or central controller. Firstly, the time embedding layer in NGM establishes the mapping relationship between irregular traffic data and regular latent temporal feature variables. Secondly, GSM collects statistical feature parameters of latent temporal feature variables from various nodes and executes graph embedding for spatial-temporal traffic data. Finally, NGM generates future traffic based on latent temporal and spatial feature variables. The introduction of the time attention mechanism enhances the framework's capability to handle irregular traffic data. Graph attention network introduces spatially correlated base station traffic feature information into local traffic prediction, which compensates for missing information in local irregular traffic data. The proposed framework effectively addresses the distributed prediction issues of irregular traffic data. By testing on real world datasets, the proposed framework improves traffic prediction accuracy by 35% compared to other commonly used distributed traffic prediction methods.
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