Mobile devices and the Internet of Things (IoT) devices nowadays generate a large amount of heterogeneous spatial-temporal data. It remains a challenging problem to model the spatial-temporal dynamics under privacy concern. Federated learning (FL) has been proposed as a framework to enable model training across distributed devices without sharing original data which reduce privacy concern. Personalized federated learning (PFL) methods further address data heterogenous problem. However, these methods don't consider natural spatial relations among nodes. For the sake of modeling spatial relations, Graph Neural Netowork (GNN) based FL approach have been proposed. But dynamic spatial-temporal relations among edge nodes are not taken into account. Several approaches model spatial-temporal dynamics in a centralized environment, while less effort has been made under federated setting. To overcome these challeges, we propose a novel Federated Adaptive Spatial-Temporal Attention (FedASTA) framework to model the dynamic spatial-temporal relations. On the client node, FedASTA extracts temporal relations and trend patterns from the decomposed terms of original time series. Then, on the server node, FedASTA utilize trend patterns from clients to construct adaptive temporal-spatial aware graph which captures dynamic correlation between clients. Besides, we design a masked spatial attention module with both static graph and constructed adaptive graph to model spatial dependencies among clients. Extensive experiments on five real-world public traffic flow datasets demonstrate that our method achieves state-of-art performance in federated scenario. In addition, the experiments made in centralized setting show the effectiveness of our novel adaptive graph construction approach compared with other popular dynamic spatial-temporal aware methods.

翻译：当前，移动设备与物联网设备生成大量异构时空数据。在隐私保护前提下建模时空动态特性仍是一个具有挑战性的问题。联邦学习被提出作为一种框架，使得模型能在分布式设备上进行训练而无需共享原始数据，从而降低隐私风险。个性化联邦学习方法进一步解决了数据异构性问题。然而，这些方法未考虑节点间固有的空间关联。为建模空间关系，已有研究提出基于图神经网络的联邦学习方法。但边缘节点间的动态时空关系尚未得到充分考虑。现有若干方法在集中式环境下建模时空动态，而在联邦学习场景下的相关探索较少。为克服这些挑战，本文提出一种新颖的联邦自适应时空注意力框架，用于建模动态时空关系。在客户端节点上，FedASTA从原始时间序列的分解项中提取时序关系与趋势模式；随后在服务器节点上，利用来自客户端的趋势模式构建自适应时空感知图，以捕捉客户端间的动态关联。此外，我们设计了融合静态图与自适应图的掩码空间注意力模块，用于建模客户端间的空间依赖性。在五个真实世界公开交通流量数据集上的大量实验表明，本方法在联邦场景下取得了最先进的性能。同时，在集中式环境下进行的实验证明，相较于其他主流动态时空感知方法，我们提出的自适应图构建方法具有显著优势。