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., 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%DIFDELCMD < \\%%% %DIF < in average.\end{abstract}
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. %DIFDELCMD < \\%%% %DIF < in average.\end{abstract}
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drives the emergence of a new
drive an emerging
D
federated learning
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. Vertical
federated learning. A class of
A
vertical federated learning,
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different fields. Decision tree models especially
many fields. In machine learning,
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are a class of
such as gradient boosting decision tree (GBDT) and random forest are
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machine learning
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are compromised in these works
is compromised in state-of-the-art vertical federated learning frameworks
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since the feature names are not exposed
with anonymous features
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due to the unprotected decision path. In this
. To address this issue in the inference process, in this
D
shall
R
, 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
to protect data privacy and allow the
R
names and thus makes the federated decision trees interpretable
meaning by concealing decision paths with a communication-efficient secure computation method for inference outputs
A
both
D
Experiments show that the inference efficiency is improved by over 50