Transverse federated learning optimization method and device based on semi-supervision and storage medium

Abstract

The invention discloses a transverse federated learning optimization method and device based on semi-supervision and a storage medium. The method comprises the steps of receiving global model parameters which are issued by a server and updated by a label-free global model this time; performing self-supervised training on a local model to be trained according to the global model parameters and thetraining samples, and updating encoder parameters and decoder parameters in the model to be trained to obtain local model parameters; sending the local model parameters to a server, so that the serverperforms supervised training on the to-be-trained model according to the local model parameters sent by each client to obtain global model parameters updated by the new label-free global model and issues the global model parameters to each client; and repeating until a preset condition is met to stop training to obtain a target model. According to the invention, transverse federated learning canbe carried out when the client only has a small amount of label data or even does not have label data completely, so that the method is suitable for a real scene lacking label data, and the labor costis saved.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a semi-supervised horizontal federated learning optimization method, equipment and storage medium. Background technique [0002] With the development of artificial intelligence, in order to solve the problem of data islands, people put forward the concept of "federated learning", so that both sides of the federation can also conduct model training to obtain model parameters without giving their own data, and can avoid data The issue of privacy breaches. Horizontal federated learning, also known as feature-aligned federated learning, is to take out the client when the data features of each client overlap more (that is, the data features are aligned) and the user overlaps less. The part of the data with the same data characteristics but not exactly the same users is subjected to joint machine learning. [0003] The current horizontal federated learning usually as...

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Problems solved by technology

[0004] The main purpose of the present invention is to provide a semi-supervised-based horizontal federated learning optimization method, device and storage medium, aiming to solve the problem that the horizontal federated learning cannot be used when there is a small amount of label data in the existing clients or even some clients have no label data. The problem of federated learning to train models

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