  The scarcity of data and isolated data islands encourage different
organizations to share data with each other to train machine learning models.
However, there are increasing concerns on the problems of data privacy and
security, which urges people to seek a solution like Federated Transfer
Learning (FTL) to share training data without violating data privacy. FTL
leverages transfer learning techniques to utilize data from different sources
for training, while achieving data privacy protection without significant
accuracy loss. However, the benefits come with a cost of extra computation and
communication consumption, resulting in efficiency problems. In order to
efficiently deploy and scale up FTL solutions in practice, we need a deep
understanding on how the infrastructure affects the efficiency of FTL. Our
paper tries to answer this question by quantitatively measuring a real-world
FTL implementation FATE on Google Cloud. According to the results of carefully
designed experiments, we verified that the following bottlenecks can be further
optimized: 1) Inter-process communication is the major bottleneck; 2) Data
encryption adds considerable computation overhead; 3) The Internet networking
condition affects the performance a lot when the model is large.
