Before revision:  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. We shall compute the candidate sets of leaf nodes based on the local data at each party in parallel, followed by securely computing the weight of the only leaf node in the intersection of the candidate sets. We propose to protect the decision path by the efficient additively homomorphic encryption method, which allows the disclosure of feature names and thus makes the federated decision trees interpretable .
After revision:  In this paper, we shall propose Fed-EINI to protect data privacy and allow the disclosure of feature names and thus makes the federated decision trees interpretable .