Model parameter verification method and apparatus in transverse federation learning, and medium

Abstract

The invention discloses a model parameter verification method, device and apparatus in transverse federated learning and a medium, which are used for solving the problems of lower model training efficiency and accuracy caused by incapability of identifying invalid model parameters in the prior art. The method specifically comprises the steps that model parameters reported by all model training apparatuses are issued to other model training apparatuses except the model training apparatuses of the model training apparatuses through a server for error evaluation; the server can obtain an error value set of each model parameter; therefore, according to the error value set of each model parameter; screening invalid model parameters, therefore, in the model training process, when the initial model parameters used in the next model training period are determined, the invalid model parameters can be eliminated, so that the problem that the model training efficiency and accuracy are low due tothe fact that the invalid model parameters cannot be recognized is effectively solved, and the model training efficiency and accuracy are improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to a method, device, equipment and medium for verifying model parameters in horizontal federated learning. Background technique [0002] "Machine learning" is one of the core research fields of artificial intelligence, and how to use the data of multiple institutions for further machine learning under the premise of satisfying privacy protection and data security is a trend in the field of machine learning research. Here In the background, the concept of "federated learning" is proposed. Among them, horizontal federated learning is a classification of federated learning. The training sample data is segmented, and the training sample data with basically the same data characteristics but not exactly the same users is selected for model training. [0003] At present, the model training system based on horizontal federated learning mainly includes model tr...

AI Technical Summary

Problems solved by technology

[0006] The embodiment of the present application provides a method, device, device and medium for verifying model parameters in horizontal federated learning, which are used to solve the problem of low efficiency and accuracy of model training due to failure to identify invalid model parameters in the prior art Specifically, the technical solutions provided by the embodiments of the present application are as follows:

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