Before revision:  However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches due to the unprotected decision path. In this paper, we shall propose Fed-EINI , an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication.
After revision:  However, the interpretability are compromised in these works such as SecureBoost since the feature names are not exposed to avoid possible data breaches . To address this issue in the inference process, in this paper, we shall propose Fed-EINI , an efficient and interpretable inference framework for federated decision tree models with only one round of multi-party communication.