Distributed LSTM-Learning from Differentially Private Label Proportions

Timon Sachweh, Daniel Boiar, T. Liebig
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Abstract

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 (LabeIProportionToLocal). The other approach extends the first one by fetching histogram data from the neighbors and joining the information with the LSTM output (LabeIProportionToDense). 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.
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基于不同自有标签比例的分布式lstm学习
近年来,数据隐私和分散的数据收集越来越受欢迎。为了解决隐私、通信带宽和从时空数据中学习的问题,我们将提出两种使用差分隐私和分散LSTM学习的高效模型:一种是学习长短期记忆(LSTM)模型,用于提取局部时间节点约束并将其馈送到致密层(LabeIProportionToLocal)。另一种方法是对第一种方法的扩展,从邻居中获取直方图数据,并将这些信息与LSTM输出(LabeIProportionToDense)连接起来。为了进行评估,使用了两个流行的数据集:Pems-Bay和metro - la。此外,我们提供了自己的数据集,该数据集基于LuST。评估将显示性能和数据隐私之间的权衡。
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