论文标题
联合转移学习：概念和应用
Federated Transfer Learning: concept and applications
论文作者
论文摘要
人工智能（AI）的发展与数据的发展固有相关。但是，在大多数行业中，数据以孤立的岛屿的形式存在，不同组织之间的共享范围有限。这是对AI进一步发展的障碍。在过去的几年中，联邦学习已成为解决此问题的一种解决方案，而不会损害用户隐私。在联合学习的不同变体中，值得注意的是联合转移学习（FTL），该学习允许知识在没有许多重叠功能和用户的域中转移知识。在这项工作中，我们对有关该主题的现有作品进行了全面的调查。在更多详细信息中，我们研究了FTL的背景及其不同的现有应用程序。我们从隐私和机器学习的角度进一步分析了FTL。
Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.
