Training and prediction methods and devices of federal transfer learning model

A technology of transfer learning and training methods, which is applied in the field of federated transfer learning model training, prediction methods and devices, can solve the problem of low model data security, achieve privacy and security, improve training efficiency, and save costs Effect

Active Publication Date: 2019-11-01
WEBANK (CHINA)
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AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a federated transfer learning model train

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  • Training and prediction methods and devices of federal transfer learning model
  • Training and prediction methods and devices of federal transfer learning model
  • Training and prediction methods and devices of federal transfer learning model

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Embodiment Construction

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0081] In order to facilitate the understanding of the embodiments of the present invention, several concepts are briefly introduced below:

[0082] At present, with the effect of deep learning in many practical applications, for example, the deep neural network model is a way of modern artificial intelligence. Supporting deep neural network models requires a large amount of high-quality labeled data. However, the screening and labeling of dat...

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Abstract

The invention relates to the technical field of artificial intelligence, in particular to training and prediction methods and devices of a federal transfer learning model. The training method comprises the following steps: a first terminal acquires an encrypted migration model issued by a parameter server in an ith training period, wherein the encrypted migration model is generated by the parameter server according to K encrypted sharing models uploaded by K terminals of K participants in an (i-1)th training period; and the first terminal updates an encryption sharing model in a first local neural network model of the first terminal according to the issued encryption migration model in the ith training period, and trains and updates the first local neural network model according to the first data. Therefore, in the federal transfer learning training process, the privacy of the data of each participant is ensured, and the training efficiency of the model is effectively improved, and theuniqueness of each terminal model is improved while the generalization ability of the model is ensured.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, in particular to a method and device for training and predicting a federated transfer learning model. Background technique [0002] With the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into technology finance (Fintech), and artificial intelligence technology is no exception. However, due to the security and real-time requirements of the financial industry, It also places higher demands on technology. [0003] With the development of Internet technology, there is a large amount and variety of information on the network platform. How to recommend personalized and interesting information services for users is very important. [0004] However, the existing local neural network models still have the risk of data leakage and user privacy leakage during the data le...

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Application Information

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IPC IPC(8): G06F21/60G06F21/62G06N20/00H04L9/08H04L29/06
CPCG06F21/602G06F21/6245G06N20/00H04L9/085H04L63/0428
Inventor 康焱刘洋陈天健
Owner WEBANK (CHINA)
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