In smart mobility, large networks of geographically distributed sensors produce vast amounts of high-frequency spatio-temporal data that must be processed in real time to avoid major disruptions. Traditional centralized approaches are increasingly unsuitable to this task, as they struggle to scale with expanding sensor networks, and reliability issues in central components can easily affect the whole deployment. To address these challenges, we explore and adapt semi-decentralized training techniques for Spatio-Temporal Graph Neural Networks (ST-GNNs) in smart mobility domain. We implement a simulation framework where sensors are grouped by proximity into multiple cloudlets, each handling a subgraph of the traffic graph, fetching node features from other cloudlets to train its own local ST-GNN model, and exchanging model updates with other cloudlets to ensure consistency, enhancing scalability and removing reliance on a centralized aggregator. We perform extensive comparative evaluation of four different ST-GNN training setups -- centralized, traditional FL, server-free FL, and Gossip Learning -- on large-scale traffic datasets, the METR-LA and PeMS-BAY datasets, for short-, mid-, and long-term vehicle speed predictions. Experimental results show that semi-decentralized setups are comparable to centralized approaches in performance metrics, while offering advantages in terms of scalability and fault tolerance. In addition, we highlight often overlooked issues in existing literature for distributed ST-GNNs, such as the variation in model performance across different geographical areas due to region-specific traffic patterns, and the significant communication overhead and computational costs that arise from the large receptive field of GNNs, leading to substantial data transfers and increased computation of partial embeddings.

翻译：在智慧交通领域，广泛分布的地理传感器网络产生海量高频时空数据，这些数据需实时处理以避免重大交通中断。传统的集中式方法日益难以胜任此任务，因其难以随传感器网络扩张而扩展，且中心组件的可靠性问题极易影响整个系统。为应对这些挑战，本研究探索并调整了适用于智慧交通领域的时空图神经网络半去中心化训练技术。我们实现了一个仿真框架：传感器按邻近性分组至多个边缘云节点，每个节点处理交通图的子图，通过从其他节点获取节点特征来训练本地ST-GNN模型，并与其他节点交换模型更新以确保一致性，从而提升可扩展性并消除对中心聚合器的依赖。我们在大规模交通数据集METR-LA和PeMS-BAY上，针对短、中、长期车速预测任务，对四种不同的ST-GNN训练配置——集中式、传统联邦学习、无服务器联邦学习及流言学习——进行了全面的对比评估。实验结果表明，半去中心化配置在性能指标上与集中式方法相当，同时在可扩展性和容错性方面具有优势。此外，我们揭示了现有分布式ST-GNN研究中常被忽视的问题：由于区域特定交通模式导致的模型在不同地理区域的性能差异，以及GNN大感受野带来的显著通信开销与计算成本，这导致大量数据传输和局部嵌入计算量的增加。