A federated learning framework for arbitrary spatio-temporal graph neural networks

The proliferation of mobile and Internet of Things (IoT) devices has resulted in a surge of time-series sensor data, posing significant challenges for centralized data collection and processing. This challenge has driven the adoption of edge computing, which offloads data processing to mid-level servers located at the edge of the Internet, thereby reducing computation and bandwidth demands. Federated learning has emerged as a promising method for training models in edge-computing environments. Recently, spatio-temporal graph neural networks (STGNNs) have shown impressive performance in time-series prediction, yet their application in edge computing is limited by the complexity of adapting them to distributed environments. To address this gap, we propose FedSTGNN (Federated Spatio-Temporal Graph Neural Network), a universal framework that converts existing centralized STGNN models into a federated learning version. We formulate the common STGNN training process using matrix operations, employ graph-based imputation methods to handle missing sensor values at edge servers, and facilitate the transition from centralized to federated STGNNs. Our comprehensive evaluations demonstrate that FedSTGNN not only preserves the prediction accuracy of the original STGNN models but is also significantly more network-efficient than the competing model. Furthermore, the framework proves its robustness in challenging real-world scenarios, including sparse graphs, long-term forecasting, and dynamic server participation. Our work presents a practical, robust, and universal solution for deploying STGNNs into various edge computing applications. • A framework for spatio-temporal graph neural network models in federated settings. • A matrix-based approach to convert STGNN training processes to federated settings. • Graph-based imputation methods compensate for missing data on edge servers. • Reduction of prediction error gap to those of original models on real-world datasets. • Reduction of training time and network usage, proving its practicality.