A Comprehensive Survey of Federated Transfer Learning: Challenges,
  Methods and Applications
Wei Guo Fuzhen Zhuang Xiao Zhang Yiqi Tong Jin Dong
Federated learning (FL) is a novel distributed machine learning paradigm that
enables participants to collaboratively train a centralized model with privacy
preservation by eliminating the requirement of data sharing. In practice, FL
often involves multiple participants and requires the third party to aggregate
global information to guide the update of the target participant. Therefore,
many FL methods do not work well due to the training and test data of each
participant may not be sampled from the same feature space and the same
underlying distribution. Meanwhile, the differences in their local devices
(system heterogeneity), the continuous influx of online data (incremental
data), and labeled data scarcity may further influence the performance of these
methods. To solve this problem, federated transfer learning (FTL), which
integrates transfer learning (TL) into FL, has attracted the attention of
numerous researchers. However, since FL enables a continuous share of knowledge
among participants with each communication round while not allowing local data
to be accessed by other participants, FTL faces many unique challenges that are
not present in TL. In this survey, we focus on categorizing and reviewing the
current progress on federated transfer learning, and outlining corresponding
solutions and applications. Furthermore, the common setting of FTL scenarios,
available datasets, and significant related research are summarized in this
survey.