Method and system for updating model parameters based on federated learning

A technology of model parameters and model data, applied in the field of model parameter update based on federated learning

Active Publication Date: 2020-11-13
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Abstract
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This puts a lot of pressure on the comm

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  • Method and system for updating model parameters based on federated learning
  • Method and system for updating model parameters based on federated learning
  • Method and system for updating model parameters based on federated learning

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[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios. Unless otherwise apparent from context or otherwise indicated, like reference numerals in the figures represent like structures or operations.

[0018] It should be understood that "system", "device", "unit" and / or "module" used in this specification is a method for distinguishing different components, elements, parts, parts or assemblies of different levels. However, the words may be replaced by other expressions if other words can achieve the same purpose.

[0019] As indicated in the specification and claims, the terms "a", "an", "a...

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Abstract

One or more embodiments of the invention relate to a method and a system for updating model parameters based on federated learning, the method and the system can be used for privacy data protection ina machine learning process, and the method is realized by any training member in participants; the method comprises the steps of obtaining a to-be-trained initial model with the same structure as other participants; performing model parameter iterative updating for multiple times, each iteration comprising: performing model training based on a self-held training sample to obtain a model data matrix corresponding to the current iteration; determining a transmission proportion corresponding to the current iteration, wherein the transmission proportion is in negative correlation with the numberof iterations; extracting a part of parameter elements from the model data matrix based on the transmission ratio; generating a parameter transmission matrix based on the partial parameter elements; uploading the parameter transmission matrix to the server; obtained updated model parameters from the server to perform a next iteration based thereon, or determining a final model based thereon.

Description

technical field [0001] One or more embodiments of this specification relate to multi-party data joint processing, and in particular to a method and system for updating model parameters based on federated learning. Background technique [0002] In data analysis, data mining, economic forecasting and other fields, machine learning models can be used to analyze and discover potential data value. Since the data held by a single data owner may be incomplete, it is difficult to accurately describe the target. In order to obtain better model prediction results, the federated learning of the model is carried out through the data cooperation of multiple data owners. has been widely used. However, with the development of federated learning technology, the calculation of model parameters calculated by federated learning is getting larger and larger, and the number of parameters is also increasing. This puts a lot of pressure on the communication during multi-party computation. [00...

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Application Information

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IPC IPC(8): G06N20/20
CPCG06N20/20
Inventor 郑龙飞王莹桂陈超超王力周俊刘健郭明宇
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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