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Model parameter determination method and device based on federal learning

A technology of model parameters and parameters, applied in the field of data processing

Active Publication Date: 2019-09-24
WEBANK (CHINA)
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The embodiment of the present application provides a method and device for determining model parameters based on federated learning, which solves the problem that the use of traditional federated learning in the prior art will be limited, and improves user experience

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  • Model parameter determination method and device based on federal learning
  • Model parameter determination method and device based on federal learning
  • Model parameter determination method and device based on federal learning

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Embodiment Construction

[0097] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of them. Based on the embodiments of the present application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0098] The method for determining model parameters based on federated learning provided by the embodiment of the present invention can be applied in figure 1 In the shown scenario architecture, the scenario architecture may include at least one terminal, and each terminal is a data owner. At least one terminal establishes a model through the federated learning system, and trains model parameters.

[0099] Terminals can be mobile phones, smart phones, no...

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Abstract

The invention discloses a model parameter determination method and device based on federal learning. The method comprises the following steps: a second terminal receiving first encrypted data sent by a first terminal, wherein the first encrypted data is obtained by encrypting label data by the first terminal according to a preset encryption algorithm; according to the first encrypted data, the sample feature data and the current feature parameters of the sample feature data, using a preset algorithm to obtain second encrypted data comprising an encryption gradient value and an encryption loss value, and receiving a first gradient value and a first loss value sent by the first terminal according to the second encrypted data; according to the first gradient value and the current characteristic parameter of the sample characteristic data, obtaining a first characteristic parameter by adopting a preset characteristic parameter algorithm; and if it is detected that the first characteristic parameter is in a convergence state according to the first loss value, determining the first characteristic parameter as a model parameter of the sample model to be trained. According to the method, a third-party collaborator is not needed, and the training efficiency is improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a method and device for determining model parameters based on federated learning. Background technique [0002] In the field of artificial intelligence, the traditional data processing model is often that one party collects data, then transfers it to another party for processing, cleaning and modeling, and finally sells the model to a third party. However, as regulations improve and monitoring becomes more stringent, operators may violate the law if the data leaves the collector or the user does not know the specific use of the model. Data exists in the form of isolated islands, and the direct solution to solving isolated islands is to integrate data into one side for processing. However, since the law does not allow operators to aggregate data roughly, in order to solve this dilemma, people have proposed "federal learning". [0003] Federated learning use...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62
CPCG06F21/62
Inventor 范涛陈天健杨强
Owner WEBANK (CHINA)
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