A federated learning method and system for vertical xgboost decision tree

Abstract

The invention discloses a federated learning method and system of a vertical xgboost decision tree, relates to the technical field of federated learning and machine learning decision trees, can ensure the privacy of feature data of samples held by all parties, and enhance the security of feature data of all parties sex. The main technical solution of the present invention is: the present invention provides the joint training process and joint reasoning process of the vertical xgboost decision tree, calculates the split point in the joint training process and discriminates each node in the joint reasoning process, and the joint training process is disclosed The information of is the maximum split value of each participant, without directly revealing the characteristic information of each participant, and the security of the joint reasoning process depends on the threshold homomorphic encryption scheme. The invention is mainly applied to the joint training and joint reasoning process of the decision tree.

Description

technical field [0001] The invention relates to the technical field of federated learning and machine learning decision tree, in particular to a federated learning method and system of vertical xgboost decision tree. Background technique [0002] Vertical xgboost decision tree federated learning means that when the feature information and label information of each sample are held by different owners, all parties jointly carry out the training of xgboost decision tree, and all parties do not want to send information to any other party during the training and inference process. Leaking feature information or label information about samples. [0003] For example, the complete sample information required for xgboost decision tree training is as follows, as shown in Table 1: [0004] ID x1 x2 x3 x4 x5 x6 y u1 ... ... ... ... ... ... ... u2 ... ... ... ... ... ... ... u3 ... ... ... ... ... ... ... [0005] Among them, ID repr...

AI Technical Summary

Problems solved by technology

[0022] As mentioned above, in the training process and the joint reasoning process of the SecureBoost scheme, there is a serious leakage of the feature information of the passive party, so in the process of longitudinal xgboost decision tree federated learning, it is difficult to protect the privacy and security of the feature information of samples from all parties sex

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