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：联邦学习涉及在手机等边缘设备上训练统计模型，使得训练数据保留在本地。联邦学习（FL）可作为训练时空模型的理想方案，这些模型依赖于异质且可能数量庞大的参与者，同时保护高度敏感的隐私位置数据。然而，将现有时空模型转化为分散式学习面临独特挑战。本综述论文回顾了现有文献中基于联邦学习的模型，这些模型被用于人类移动预测、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工作所采用的指标和数据集，并建立了这些方法与集中式设置相比的基线。最后，我们讨论了在分散式环境中应用时空模型的挑战，并通过突出文献中的空白，为研究界提供了路线图和机遇。