Model training method and device based on federated learning and server

A model training and server node technology, applied in the computer field, can solve problems such as slow convergence speed and low model accuracy, and achieve the effects of accelerating convergence, reducing weight coefficients, and ensuring security.

Pending Publication Date: 2019-11-12
PEKING UNIV SHENZHEN GRADUATE SCHOOL
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The embodiment of the present invention provides a model training method, device and server based on federated learning to

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  • Model training method and device based on federated learning and server
  • Model training method and device based on federated learning and server
  • Model training method and device based on federated learning and server

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Embodiment Construction

[0046] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the application are not shown or described in the description, this is to avoid the core part of the application being overwhelmed by too many descriptions, and for those skilled in the art, it is necessary to describe these operations in detail Relevant operations are not necessary, and they can fully understand the relevant operations according to the description in the specification and genera...

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Abstract

The embodiment of the invention provides a model training method and device based on federated learning and a server. The method comprises the steps of sending a to-be-trained model to a plurality ofworking nodes; receiving a local model fed back by the plurality of working nodes, wherein the local model is obtained by training a to-be-trained model by each working node according to own data; determining the precision of each local model according to the test data set; determining a weight coefficient of each local model according to the precision of each local model, wherein the weight coefficient is positively correlated with the precision; and updating the to-be-trained model according to the plurality of local models and the corresponding weight coefficients. According to the method provided by the embodiment of the invention, by increasing the weight coefficient of the high-precision local model and reducing the weight coefficient of the low-precision local model, the convergencerate of the to-be-trained model is increased, and the precision of the to-be-trained model is improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a model training method, device and server based on federated learning. Background technique [0002] On the one hand, with the continuous improvement of laws and regulations and the continuous strengthening of monitoring, the centralized processing of data will face huge legal risks; on the other hand, various data owners are unwilling to share raw data due to factors such as safety and economic interests. These factors will cause data to exist in the form of islands. In order to break the data island, federated learning came into being. [0003] Federated learning does not require all data owners to share the original data, and can make full use of the original data of each data owner for model training under the condition of ensuring security, effectively solving the data island problem in the era of artificial intelligence. The current model ...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06F21/62G06K9/62G06K9/00
CPCG06F9/5027G06F21/6245G06F21/6218G06V10/95G06F18/214
Inventor 雷凯黄济乐方俊杰
Owner PEKING UNIV SHENZHEN GRADUATE SCHOOL
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