The advancement of federated learning (FL) has enabled model training in spatio-temporal graphs (STG) while addressing privacy concerns that restrict direct data sharing. However, capturing spatio-temporal dependencies in FL settings that involve subgraphs remains challenging. On one hand, nodes in a subgraph struggle to model spatio-temporal correlations across different subgraphs. On the other hand, temporal data (e.g., traffic flows) often exhibits distinct non-i.i.d. patterns across subgraphs. To address these challenges, we propose a novel framework called Federated Subgraph Learning over Spatio-Temporal Graphs (FedSTG) for effective traffic prediction. FedSTG leverages attention-based models and includes two key modules. First, the Isolated Nodes Connection module integrates and filters the cluster centers of node embeddings from other subgraphs to enhance local model training, thereby capturing spatio-temporal correlations between nodes across subgraphs. Second, the Personalized Model Aggregation module compute the similarities between clients pairwise using their local models and aggregates personalized models based on the similarities as aggregation weights, thereby ensuring the effective model aggregation. Extensive experiments on two attention-based models and three commonly used traffic prediction datasets demonstrate the superiority of FedSTG.

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.

While federated learning has shown strong results in opti- mizing a machine learning model without direct access to the original data, its performance may be hindered by in- termittent client availability which slows down the conver- gence and biases the final learned model. There are significant challenges to achieve both stable and bias-free training un- der arbitrary client availability. To address these challenges, we propose a framework named Federated Graph-based Sam- pling (FEDGS), to stabilize the global model update and mitigate the long-term bias given arbitrary client availabil- ity simultaneously. First, we model the data correlations of clients with a Data-Distribution-Dependency Graph (3DG) that helps keep the sampled clients data apart from each other, which is theoretically shown to improve the approximation to the optimal model update. Second, constrained by the far- distance in data distribution of the sampled clients, we fur- ther minimize the variance of the numbers of times that the clients are sampled, to mitigate long-term bias. To validate the effectiveness of FEDGS, we conduct experiments on three datasets under a comprehensive set of seven client availability modes. Our experimental results confirm FEDGS’s advantage in both enabling a fair client-sampling scheme and improving the model performance under arbitrary client availability. Our code is available at https://github.com/WwZzz/FedGS.

Road markings extraction (RME) from 3D point clouds acquired by mobile LiDAR systems has been widely used for road safety and autonomous driving. However, due to the increasing awareness of personal data protection and national informat ion security regulations, most autonomous driving companies are not willing to share their private point clouds data with the community. Therefore, such restriction of centralized training might inevitably inhibit the effectiveness of RME procedure. Federated learning (FL) is a distributed machine learning architecture that could address the aforementioned privacy-accuracy dilemma to collaboratively learn a global RME model from multiple clients without sharing raw data. In this paper, we propose a novel FedRME, a federated road markings extraction system to collaboratively learn a global RME model with multiple privacy-preserved local models from 3D mobile LiDAR point clouds. FedRME adopt the classical FedAvg model to construct a generalizable global feature embedding model without accessing local data. Moreover, to tackle data heterogeneity problem that local models vary in point clouds volumes and categories, we design a dynamic weighting mechanism to optimize the cooperative training effectiveness before server aggregation. Experimental results on three real-world mobil e LiDAR point clouds datasets with federated learning settings demonstrate that FedRME not only achieves superior performance but also reduces computation by up to 25%.The source code is available at https://github.com/WwZzz/easyFL#FedRME.