  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 great variety of business cooperation
among enterprises in many fields. 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.
However, stateof-art vertical federated learning frameworks adapt anonymous
features to avoid possible data breaches, makes the interpretability of the
model compromised. To address this issue in the inference process, in this
paper, we firstly make a problem analysis about the necessity of disclosure
meanings of feature to Guest Party in vertical federated learning. Then we find
the prediction result of a tree could be expressed as the intersection of
results of sub-models of the tree held by all parties. 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. The advantages of Fed-EINI will be
demonstrated through both theoretical analysis and extensive numerical results.
We improve the interpretability of the model by disclosing the meaning of
features while ensuring efficiency and accuracy.
