Method, apparatus, and system for training tree model

A method applied to a second apparatus, comprising:

• determining, for a first node of a tree model, a segmentation result of a first segmentation policy of the second apparatus for each sample in a sample set, wherein the sample set comprises samples for training the tree model;
• determining an encrypted intermediate parameter corresponding to the first segmentation policy based on the segmentation result of the first segmentation policy for the sample set and encrypted label distribution information for the first node, wherein the encrypted label distribution information for the first node is determined based on encrypting first label information of the sample set and encrypting first distribution information of the sample set for the first node, the first label information comprises label data of each sample in the sample set, and the first distribution information comprises indication data indicating whether each sample belongs to the first node;
• and
• sending the encrypted intermediate parameter corresponding to the first segmentation policy to a first apparatus, wherein the encrypted intermediate parameter is used to determine a preferred segmentation policy of the first node.

A first apparatus, comprising:

• at least one processor;
• and
• a non-transitory computer readable medium comprising instructions which, when executed by the at least one processor, cause the processor to:
• determine, for a first node of a tree model, a first gain corresponding to a segmentation policy of the first apparatus;
• receive, via a communication device coupled to the processor, a first encrypted intermediate parameter corresponding to a first segmentation policy of a second apparatus for the first node and sent by the second apparatus, wherein the first encrypted intermediate parameter is determined based on encrypted label distribution information for the first node and a segmentation result of the first segmentation policy for each sample in a sample set, the sample set comprises samples for training the tree model, the encrypted label distribution information is determined based on encrypting first label information of the sample set and encrypting first distribution information of the sample set for the first node, the first label information comprises label data of each sample in the sample set, and the first distribution information comprises indication data indicating whether each sample in the sample set belongs to the first node;
• and
• determine a preferred segmentation policy of the first node based on the first gain corresponding to the segmentation policy of the first apparatus and a second gain corresponding to a second segmentation policy of the second apparatus for the first node, wherein the first segmentation policy comprises the second segmentation policy, and the second gain corresponding to the second segmentation policy is determined based on a second encrypted intermediate parameter corresponding to the second segmentation policy.