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

The increasing concerns about data privacy and security drives the emergence of a new field of studying privacy-preserving machine learning from isolated data sources, i.e., \textit{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 different fields. Decision tree models especially decision tree ensembles are a class of widely applied powerful machine learning models with high interpretability and modeling efficiency. 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. 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. The advantages of Fed-EINI will be demonstrated through theoretical analysis and extensive numerical results. Experiments show that the inference efficiency is improved by over $50\%$ in average.