One‐class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. Autoencoder is the type of neural network that has been widely applied in these one‐class problems. In the Big Data era, new challenges have arisen, mainly related with the data volume. Another main concern derives from Privacy issues when data is distributed and cannot be shared among locations. These two conditions make many of the classic and brilliant methods not applicable. In this paper, we present distributed singular value decomposition (DSVD‐autoencoder), a method for autoencoders that allows learning in distributed scenarios without sharing raw data. Additionally, to guarantee privacy, it is noniterative and hyperparameter‐free, two interesting characteristics when dealing with Big Data. In comparison with the state of the art, results demonstrate that DSVD‐autoencoder provides a highly competitive solution to deal with very large data sets by reducing training from several hours to seconds while maintaining good accuracy.

中文翻译：

---

DSVD-autoencoder：一种用于一类分类的可扩展分布式隐私保护方法

一类分类作为解决各种真实环境（如异常或新奇检测）中典型问题的解决方案而引起了人们的兴趣。自编码器是一种广泛应用于这些单类问题的神经网络。大数据时代，出现了新的挑战，主要与数据量有关。另一个主要问题来自数据分布且无法在不同位置共享时的隐私问题。这两个条件使许多经典和辉煌的方法不适用。在本文中，我们提出了分布式奇异值分解（DSVD-autoencoder），这是一种自动编码器方法，它允许在不共享原始数据的情况下在分布式场景中学习。此外，为了保证隐私，它是非迭代和无超参数的，处理大数据时的两个有趣特征。与现有技术相比，结果表明 DSVD 自动编码器提供了一种极具竞争力的解决方案，通过将训练时间从几小时减少到几秒钟，同时保持良好的准确性，来处理非常大的数据集。