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Growing Trees Together Privately: An Introduction to Gradient Boosted Decision Tree Models for Federated  Learning

Federated Learning (FL) is a method for training machine learning algorithms without directly sharing raw data distributions across different parties. However, many of the recently proposed methods for FL have focused strictly on linear models, neural networks, and kernel-based approaches. In this talk, we introduce the implementation of a novel Gradient Boosted Decision Tree algorithm for Federated Learning. We specifically highlight some of the key advantages tree-based models offers within a FL setting, as well as demonstrating how they complement some of the desirable functionalities which FL aims to provide out of the box.