Data privacy and decentralised data collection has become more and more popular in recent years. In order to solve issues with privacy, communication bandwidth and learning from spatio-temporal data, we will propose two efficient models which use Differential Privacy and decentralized LSTM-Learning: One, in which a Long Short Term Memory (LSTM) model is learned for extracting local temporal node constraints and feeding them into a Dense-Layer (LabelProportionToLocal). The other approach extends the first one by fetching histogram data from the neighbors and joining the information with the LSTM output (LabelProportionToDense). For evaluation two popular datasets are used: Pems-Bay and METR-LA. Additionally, we provide an own dataset, which is based on LuST. The evaluation will show the tradeoff between performance and data privacy.

翻译：为了解决隐私、通信带宽和从时空数据中学习的问题,我们将提出两种高效模式,使用不同的隐私和分散的LSTM-学习:一种是学习长期短期内存模式,以提取当地的时间节点限制并将其输入Dense-Layer(LabelProportion to Contlorial),另一种办法是从邻居那里获取直方图数据,并将信息与LSTM输出(LabelProportionTodense)连接起来,从而扩展第一种方法。在评价中使用两种流行数据集:Pems-Bay和METR-LA。此外,我们提供一套以LuST为基础的自己的数据集。评价将显示业绩与数据隐私之间的权衡。