Model parameter training method and device based on federal learning

Abstract

The invention discloses a model parameter training method and device based on federal learning. The method comprises the steps of using a first terminal to receive a first encryption mapping model sent by a second terminal; predicting the missing feature of the first sample data according to the first encryption mapping model to obtain the first encryption complement sample data; training a federal learning model according to the current encryption model parameters, the first sample data and the first encryption completion sample data, and obtaining a first secret sharing loss value and a first secret sharing gradient value; and if it is detected that the federal learning model is in a convergence state, obtaining a target model parameter according to the updated first secret sharing model parameter corresponding to the first secret sharing gradient value and a second secret sharing model parameter sent by the second terminal. According to the method, by adopting a secret sharing mode, the training process of the federated learning model does not need the assistance of a second collaborator, and the user experience is improved.

Description

technical field

[0001] The present application relates to the technical field of data processing, and in particular to a model parameter training method and device 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 uses techni...

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