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1. CN111523673 - Model training method, device and system

Office
Chine
Numéro de la demande 201910103212.5
Date de la demande 01.02.2019
Numéro de publication 111523673
Date de publication 11.08.2020
Numéro de délivrance 111523673
Date de délivrance 27.07.2021
Type de publication B
CIB
G06N 20/00
GPHYSIQUE
06CALCUL; COMPTAGE
NSYSTÈMES DE CALCULATEURS BASÉS SUR DES MODÈLES DE CALCUL SPÉCIFIQUES
20Apprentissage automatique
CPC
G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G06N 5/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5Computer systems using knowledge-based models
04Inference methods or devices
Déposants ALIBABA GROUP HOLDING LTD.
创新先进技术有限公司
Inventeurs CHEN CHAOCHAO
陈超超
LI LIANG
李梁
ZHOU JUN
周俊
Mandataires 北京永新同创知识产权代理有限公司 11376
Titre
(EN) Model training method, device and system
(ZH) 模型训练方法、装置及系统
Abrégé
(EN)
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.

(ZH)
本公开提供用于训练线性/逻辑回归模型的方法和装置,该方法包括:执行下述迭代过程,直到满足预定条件:基于各个训练参与方的当前子模型以及对应的特征样本子集,使用秘密共享矩阵加法来获得线性/逻辑回归模型的当前预测值;确定当前预测值与对应的标记值之间的预测差值并发送给各个第二训练参与方,以供在各个第二训练参与方处来更新各自的当前子模型;以及基于第一训练参与方的当前子模型以及对应的特征样本子集与所确定出的预测差值之积来更新第一训练参与方的当前子模型。在迭代过程未结束时,更新后的各个训练参与方的当前子模型被用作下一迭代过程的当前子模型。该方法能够在保证各方数据安全的情况下提高模型训练的效率。

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