论文标题
联合学习 - 方法，应用程序及以后
Federated Learning -- Methods, Applications and beyond
论文作者
论文摘要
近年来，由于大量可用数据和技术进步，机器学习模型的应用迅速增加。虽然某些领域（例如Web分析）可以从中受益，但只有较小的限制，但其他领域（例如在医学中使用患者数据的领域）却进行了强烈调控。特别是\ emph {数据隐私}在立法中对欧盟或一般隐私权法规的可信赖AI倡议最近强调了重要角色。另一个主要的挑战是，所需的培训\ emph {data是}通常，\ emph {distributioned}在功能或样本方面，对于古典batch学习方法而言不可用。在2016年，Google提出了一个框架，称为\ emph {联合学习}，以解决这两个问题。我们简要概述了垂直和水平\ emph {联合学习}领域的现有方法和应用程序，以及\ emph {fdedrated Transver Learning}。
In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress.While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated. In particular \emph{data privacy} plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training \emph{data is} often \emph{distributed} in terms of features or samples and unavailable for classicalbatch learning approaches. In 2016 Google came up with a framework, called \emph{Federated Learning} to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal \emph{Federated Learning}, as well as \emph{Fderated Transfer Learning}.
