Model updating method, working node and model updating system

A technology for updating working nodes and models, applied in the field of artificial intelligence, can solve problems such as relying on central nodes, and achieve the effects of fast calculation speed, reduced network communication overhead, and small delay

Pending Publication Date: 2021-04-13
HUAWEI TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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But the existing federated learning ...

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  • Model updating method, working node and model updating system
  • Model updating method, working node and model updating system
  • Model updating method, working node and model updating system

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Embodiment Construction

[0049] The technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the accompanying drawings.

[0050] Embodiments of the present application provide a method for updating a model, a working node, and a system for updating a model. This method does not depend on the central node. After being executed on a working node, it can combine the saved data (local data) of the working node and other working nodes in the neighborhood (belonging to the same subnetwork as the working node) other working nodes) to update the machine learning model on the working node (for example, a deep neural network model, a support vector machine (support vector machine, SVM) model, and other machine learning models based on gradient updates). In this way, there is no need to upload a large amount of user data to the cloud, and the privacy and security of users can be guaranteed. In addition, the working nodes in this method a...

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Abstract

The invention relates to a model updating method in the field of artificial intelligence (AI), and can provide a flexible and efficient optimization technology for a decentralized distributed AI system. The method can update the local model through the interaction of the working nodes in the neighborhood. According to the method, the model can be updated by receiving one or more kinds of information of the at least one parameter, the gradient and the impulse of the model transmitted by other working nodes in the neighborhood and combining the local model and the data.

Description

technical field [0001] This application relates to the field of artificial intelligence (AI), and mainly relates to a model updating method, working nodes and a model updating system. Background technique [0002] Machine learning systems are the most important branch of AI systems. Distributed machine learning (distributed machine learning, DML) system is a commonly used system for processing large-scale artificial intelligence application tasks. Traditional distributed machine learning systems are centralized systems that use computing clusters to obtain predictive models by training massive user data. Specifically, the central node schedules each working node to calculate the gradient of the loss function with respect to the model; after the calculation is completed, let all the working nodes upload the gradient to the central node; the central node updates the model after receiving the uploaded gradient. In the process of use, users need to request services from the sy...

Claims

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Application Information

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IPC IPC(8): G06N20/00G06F8/65
CPCG06F8/65G06N20/00
Inventor 朱越张宝峰王成录
Owner HUAWEI TECH CO LTD
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