Model parameter training method, terminal, system and medium based on federated learning

Abstract

The invention discloses a model parameter training method based on federal learning, a terminal, a system and a medium, and the method comprises the steps: determining a feature intersection of a first sample of a first terminal and a second sample of a second terminal, training the first sample based on the feature intersection to obtain a first mapping model, and sending the first mapping modelto the second terminal; receiving a second encryption mapping model sent by a second terminal, and predicting the missing feature part of the first sample to obtain a first encryption completion sample; receiving a first encrypted federal learning model parameter sent by a third terminal, training a to-be-trained federal learning model according to the first encrypted federal learning model parameter, and calculating a first encryption loss value; sending the first encryption loss value to a third terminal; and when a training stopping instruction sent by the third terminal is received, takingthe first encrypted federal learning model parameter as a final parameter of the federal learning model to be trained. According to the invention, the characteristic space of two federated parties isexpanded by using transfer learning, and the prediction capability of the federated model is improved.

Description

technical field [0001] The present invention relates to the technical field of data processing, in particular to a model parameter training method, terminal, system and medium 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, with the improvement of regulations and stricter monitoring, operators may violate the law if the data leaves the collector or the user does not know the specific purpose 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, doing so is now likely to be illegal because the law does not allow operators to aggregate data indiscriminately. [0003] To solve this dilemma, people have propo...

AI Technical Summary

Problems solved by technology

[0004] However, the existing horizontal federation method can only be applied to the case where both A and B samples of the federation are marked, and the feature dimensions of both sides are the same, but it is not applicable to the case where the feature dimensions of A and B are different

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