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Model training method, device and system

A model and sub-model technology, applied in the field of machine learning, can solve the problem of low model training efficiency and achieve the effect of improving efficiency

Active Publication Date: 2020-08-11
ADVANCED NEW TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the model training efficiency of existing machine learning methods that can protect data security is low

Method used

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  • Model training method, device and system
  • Model training method, device and system
  • Model training method, device and system

Examples

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Embodiment Construction

[0027] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and realize the subject matter described herein, and is not intended to limit the protection scope, applicability or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as needed. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may also be combined in other examples.

[0028] As used herein, the term "comprising" and its variants represent open terms meaning "including but not limited to". The...

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Abstract

The invention provides a method and a device for training a linear / logistic regression model. The method comprises the steps of: executing the following iterative processes till a preset condition ismet: acquiring a current prediction value of the linear / logistic regression model through employing secret sharing matrix addition based on a current sub-model of each training participant and a corresponding feature sample subset; determining a prediction difference value between the current prediction value and a corresponding mark value, and transmitting the prediction difference value to eachsecond training participant so as to update the respective current sub-model at each second training participant; and updating the current sub-model of the first training participant based on the current sub-model of the first training participant and the product of the corresponding feature sample subset and the determined prediction difference. When the iteration process is not finished, the updated current sub-model of each training participant is used as the current sub-model of the next iterative process. According to the method, the model training efficiency can be improved under the condition of ensuring the data security of each party.

Description

technical field [0001] The present disclosure relates generally to the field of machine learning, and more particularly to methods, devices, and systems for collaboratively training linear / logistic regression models via multiple training participants using vertically split training sets. Background technique [0002] Linear regression model and logistic regression model are regression / classification models widely used in the field of machine learning. In many cases, multiple model training participants (eg, e-commerce companies, courier companies, and banks) each have a different portion of the data for the feature samples used to train the linear / logistic regression model. The multiple model training participants usually want to jointly use each other's data to uniformly train the linear / logistic regression model, but do not want to provide their respective data to other model training participants to prevent their own data from being leaked. [0003] Faced with this situa...

Claims

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Application Information

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IPC IPC(8): G06N20/00
CPCG06N20/00G06N20/10G06N5/04
Inventor 陈超超李梁周俊
Owner ADVANCED NEW TECH CO LTD
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