Federation learning method, device and equipment and storage medium

Abstract

The invention relates to a federated learning method, device and equipment and a storage medium. The method comprises the steps of obtaining task configuration information of at least two clients participating in a target federated learning task, wherein the task configuration information is obtained by summarizing task configuration data received by the client; initializing model training configuration information in the target federated learning task according to the task configuration information; training configuration information based on the initialized model; and executing a model training operation of the target federated learning task, and sending model updating parameters generated when the model training operation is executed to the corresponding clients, so as to enable the clients to update local federated learning models to obtain trained federated learning models corresponding to the clients. The method relates to the field of machine learning. By adopting the method, the usability of federated learning can be improved.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a federated learning method, device, device and storage medium. Background technique [0002] Federated learning is also known as federated machine learning, federated learning, and federated learning. Federated Machine Learning is a machine learning framework that can effectively help multiple agencies conduct data usage and machine learning modeling while meeting the requirements of user privacy protection, data security, and government regulations. [0003] When traditional technologies perform federated learning, users often need to perform a series of tedious debugging tasks for federated learning tasks, such as manually adjusting the model parameters of the model to be trained, manually performing feature engineering, etc., the user's professional knowledge and modeling experience All have higher requirements and poor usability. [0004] Therefore, ther...

AI Technical Summary

Problems solved by technology

[0003] When traditional technologies perform federated learning, users often need to perform a series of tedious debugging tasks for federated learning tasks, such as manually adjusting the model parameters of the model to be trained, manually performing feature engineering, etc., the user's professional knowledge and modeling experience All have higher requirements and poor usability

[0004] Therefore, there is a problem of poor usability in federated learning in the prior art

Images