GBDT and LR fusion method and device based on federated learning, equipment and storage medium

Abstract

The invention relates to a GBDT and LR fusion method and device based on federated learning, equipment and a storage medium, and the method comprises the steps: calculating the gradient of each firstsample, encrypting the gradient, and transmitting the encrypted gradient to a passive side; obtaining the encrypted gradient and group of each group of the passive party; decrypting the gradient sum group, selecting optimal feature division according to the gradient sum, and transmitting a division value corresponding to the optimal feature division to a passive party; obtaining a sample space inwhich the passive party is divided into a left node or a right node; splitting the first sample according to the sample space to obtain a tree structure corresponding to the GBDT model; and constructing a feature matrix according to the tree structure, and performing logistic regression training to obtain an LR model. By means of the GBDT and LR fusion method, device and equipment based on federated learning and the storage medium, financial data can be directly aggregated for fusion model training of GBDT and LR models.

Description

technical field [0001] This application relates to the technical field of model hosting, and in particular to a federated learning-based GBDT and LR fusion method, device, device and storage medium. Background technique [0002] In financial scenarios, the construction of some risk control models is often involved, and because the industry needs models with high interpretability, simple and effective logistic regression is often used to deal with classification problems. However, logistic regression is a linear model, which cannot capture nonlinear information. It requires a lot of feature engineering and consumes manpower and material resources. GBDT (Gradient BoostDecision Tree, Gradient Boosting Tree) can just be used to discover distinguishable features and feature combinations. Reduce labor costs in feature engineering. But correspondingly, GBDT is an ensemble method, so it is less interpretable. The fusion model of GBDT and LR (Logistic Regression, generalized linear...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of this application is to provide a GBDT and LR fusion method, device, equipment and storage medium based on federated learning, aiming to solve the technical problem that financial data cannot be directly aggregated for fusion model training of GBDT and LR models

Images