Quantifying the Performance of Federated Transfer Learning

Understand

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.