  It is commonly observed that the data are scattered everywhere and difficult
to be centralized. The data privacy and security also become a sensitive topic.
The laws and regulations such as the European Union's General Data Protection
Regulation (GDPR) are designed to protect the public's data privacy. However,
machine learning requires a large amount of data for better performance, and
the current circumstances put deploying real-life AI applications in an
extremely difficult situation. To tackle these challenges, in this paper we
propose a novel privacy-preserving federated machine learning model, named
Federated Extra-Trees, which applies local differential privacy in the
federated trees model. A secure multi-institutional machine learning system was
developed to provide superior performance by processing the modeling jointly on
different clients without exchanging any raw data. We have validated the
accuracy of our work by conducting extensive experiments on public datasets and
the efficiency and robustness were also verified by simulating the real-world
scenarios. Overall, we presented an extensible, scalable and practical solution
to handle the data island problem.

Machine Learning;Cryptography and Security;Machine Learning