Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly sensitive location data. However, there are unique challenges involved with transitioning existing spatial temporal models to decentralized learning. In this survey paper, we review the existing literature that has proposed FL-based models for predicting human mobility, traffic prediction, community detection, location-based recommendation systems, and other spatial-temporal tasks. We describe the metrics and datasets these works have been using and create a baseline of these approaches in comparison to the centralized settings. Finally, we discuss the challenges of applying spatial-temporal models in a decentralized setting and by highlighting the gaps in the literature we provide a road map and opportunities for the research community.

翻译：联邦学习涉及在移动电话等边缘设备上训练统计模型，同时保持训练数据本地存储。联邦学习（FL）可作为训练时空模型的理想方案，这些模型依赖异构且潜在海量的参与者，同时需保护高度敏感位置数据的隐私。然而，将现有时空模型迁移至分散式学习面临独特挑战。本综述论文系统梳理了现有文献中基于联邦学习的模型，这些模型应用于人类移动性预测、交通预测、社区检测、基于位置的推荐系统及其他时空任务。我们描述了这些研究所采用的指标与数据集，并建立这些方法相对于集中式设置的基准对比。最后，我们探讨了在分散式环境中应用时空模型的挑战，通过指出文献中的研究空白，为学术界提供了路线图与发展机遇。