Federated Transfer Learning is a method where multiple computers or devices learn without sharing their data with each other.
Imagine different clinics want to train a computer program together that detects diseases, but they are not allowed to exchange their patient data.
With Federated Transfer Learning, these clinics can share their knowledge without sharing personal data.
Each trains the program with their own data, and the program learns better overall because it learns from all together, but the data remains secure and private.
This way, many different locations can collaborate without privacy issues.