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Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Federated Learning [article]

Xiaolin Chen, Shuai Zhou, Bei guan, Kai Yang, Hao Fan, Hu Wang, Yongji Wang
2021 arXiv   pre-print
In machine learning, decision tree ensembles such as gradient boosting decision trees (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency.  ...  With this key observation, we protect data privacy and allow the disclosure of feature meaning by concealing decision paths and adapt a communication-efficient secure computation method for inference outputs  ...  Fed-EINI: An Efficient and Interpretable Inference Framework for Decision Tree Ensembles in Vertical  ... 
arXiv:2105.09540v11 fatcat:o4ppvrvje5g3bekmpk2o67aj2y