Multi-party joint modeling method and device based on federated learning and medium

A modeling method and federated technology, applied in the field of machine learning of financial technology, can solve problems such as low computational efficiency

Pending Publication Date: 2020-07-03
WEBANK (CHINA)
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Problems solved by technology

[0005] The main purpose of this application is to provide a multi-party joint modeling method, device and medium based on federated learning, aiming to solve the technical problem of low computational efficiency in the construction of vertical federated modeling algorithm models in the prior art

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  • Multi-party joint modeling method and device based on federated learning and medium
  • Multi-party joint modeling method and device based on federated learning and medium
  • Multi-party joint modeling method and device based on federated learning and medium

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[0080] It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

[0081] The embodiment of the present application provides a multi-party joint modeling method based on federated learning. The multi-party joint modeling method based on federated learning is applied to the first device for vertical federation. In this application, the multi-party joint modeling method based on federated learning For the first example, refer to figure 1 , the multi-party joint modeling method based on federated learning includes:

[0082] Step S10, performing sample alignment on each second device associated with the first device to obtain first sample data;

[0083] In this embodiment, it should be noted that the second device can communicate with the first device, and the first device and each of the second devices can perform longitudinal federated learning, and in this embodi...

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Abstract

The invention discloses a multi-party joint modeling method and device based on federated learning and a medium. The multi-party joint modeling method based on federated learning comprises the steps that multi-party joint modeling is carried out, and performing sample alignment with each second device associated with the first device to obtain first sample data, interacting with each second devicebased on the first sample data to perform longitudinal federation, and calculating a feature splitting gain histogram to construct a joint modeling decision tree. The technical problem that the calculation efficiency is low when a longitudinal federation modeling algorithm model is constructed is solved.

Description

technical field [0001] The present application relates to the field of machine learning technology of financial technology (Fintech), and in particular to a multi-party joint modeling method, device and medium based on federated learning. Background technique [0002] With the continuous development of financial technology, especially Internet technology finance, more and more technology [0003] (such as distributed, blockchain, artificial intelligence, etc.) are applied in the financial field, but the financial industry also puts forward higher requirements for technology, such as the distribution of corresponding to-do items in the financial industry. [0004] With the continuous development of computer software and artificial intelligence, the application of machine learning modeling is becoming more and more extensive. Among them, the GBDT (Gradient Boosting Decision Tree, gradient boosting iterative decision tree) algorithm is often used in application scenarios such a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/24323G06F18/214
Inventor 马国强范涛魏文斌谭明超郑会钿陈天健杨强
Owner WEBANK (CHINA)
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