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

The increasing concerns about data privacy and security drive an emerging field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning. A class of federated learning, vertical federated learning, where different parties hold different features for common users, has a great potential of driving a more variety of business cooperation among enterprises in many fields. In machine learning, decision tree ensembles such as gradient boosting decision tree (GBDT) and random forest are widely applied powerful models with high interpretability and modeling efficiency. However, the interpretability is compromised in state-of-the-art vertical federated learning frameworks such as SecureBoost with anonymous features to avoid possible data breaches. To address this issue in the inference process, in this paper, we propose Fed-EINI to protect data privacy and allow the disclosure of feature meaning by concealing decision paths with a communication-efficient secure computation method for inference outputs. The advantages of Fed-EINI will be demonstrated through both theoretical analysis and extensive numerical results.