Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electorencephal data rich with spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract hidden spatial-temporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires a colossal amount of training data in order to obtain satisfactory model performance. Considering the above-mentioned challenges, we propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning in this paper. After transforming the raw spatial-temporal data into high quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs capable of capturing the hidden topological and long-term temporal correlation information in these graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module in our framework to leverage diverse data distributions from different participants with attentive aggregations of their models.

翻译：时空数据包含丰富的信息，近年来由于相关应用在各领域的快速发展而受到广泛研究。例如，医疗机构常通过附着在患者不同部位的电极分析富含空间和时间特征的脑电图数据，用于健康评估和疾病诊断。现有研究主要采用卷积神经网络（CNN）或循环神经网络（RNN）等深度学习技术提取隐藏的时空特征。然而，同时融合空间信息的相互依赖性与时间动态变化仍具挑战性。实际上，对于利用这些时空特征完成复杂预测任务的模型而言，通常需要海量训练数据才能获得满意的模型性能。针对上述挑战，本文提出一种自适应联邦关联框架FedRel用于时空图学习。该框架的核心动态内-间图（DIIG）模块在将原始时空数据转化为高质量特征后，能够利用这些特征生成可捕捉图中隐藏拓扑结构和长期时间相关信息的时空图。为在保护本地数据隐私的同时提升模型泛化能力与性能，我们还设计了关联驱动的联邦学习模块，通过注意力聚合参与者模型的权重，充分利用不同参与者的多样化数据分布。