Designed for decision tree, the National University of Singapore and Tsinghua University jointly proposed a fast and safe federal learning system

Bond learning is a very hot field in machine learning , It refers to the joint training model of multiple parties without transmitting data . With the development of federal learning , Federal learning systems are also emerging , for example FATE, FedML, PaddleFL, TensorFlow-Federated wait .

However , Most federated learning systems do not support federated learning training of tree models . Compared to neural networks , Tree model has fast training , Strong explanatory ability , Suitable for tabular data . Tree model in finance , Medical care , The Internet and other fields have a wide range of application scenarios , For example, it is used for advertising and recommendation 、 Stock forecast, etc .

The representative model of decision tree is Gradient Boosting Decision Tree (GBDT). Due to the limited prediction ability of a tree ,GBDT adopt boosting The method trains multiple trees in series , Through the way that each tree is used to fit the residuals of the current predicted value and the tag value , Finally, a good prediction effect is achieved . Representative GBDT The system has XGBoost, LightGBM, CatBoost, ThunderGBM, among XGBoost Many times KDD cup Used by the champion team . However , None of these systems support the... In the context of Federated learning GBDT Training .

In recent days, , Researchers from National University of Singapore and Tsinghua University proposed a new federal learning system focusing on training tree model  FedTree.

• Address of thesis ： https://github.com/Xtra-Computing/FedTree/blob/main/FedTree_draft_paper.pdf
• Project address ： https://github.com/Xtra-Computing/FedTree