  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.
