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 that global model parameters updated by a global model this time and issued by a server side are received; after the first model is updated based on the global model parameters, self-supervised training on the first model is performedbased on a local label-free sample and an augmented sample of the label-free sample to obtain local model parameters; the local model parameters are sent to a server, so that the server performs supervised training on the second model according to the labeled samples and the local model parameters received from each client to obtain new global model parameters updated by the global model and issues the global model parameters to each client; and the process is repeated until a preset condition is met to stop training to obtain a target model. According to the invention, transverse federatedlearning can be carried out when only a small number of labeled samples exist at the server side and no label data exists at the client side, so that the method is suitable for a real scene lacking label data and the labor cost is 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...

AI Technical Summary

Problems solved by technology

[0004] The main purpose of the present invention is to provide a semi-supervised horizontal federated learning optimization method, device and storage medium, aiming to solve the problem that the horizontal federated learning cannot be used to train the model in the case of unlabeled data in the existing client

Images