Federated learning method, device, computing device and storage medium

Abstract

The embodiment of the application discloses a federated learning method, device, computing device and storage medium, which belong to the field of computer technology. The method includes: the second computing device trains the federated learning model in parallel through a plurality of computing unit groups; the second computing device sends the local updating information to the first computing device in response to the target computing unit group obtaining local update information of the federated learning model; The first computing device receives local update information sent by at least one second computing device, determines global update information according to the received local update information, and sends global update information to at least one second computing device; the second computing device updates according to the global update information Federated Learning Model. In the embodiment of the present application, the local update information is sent based on the computing unit group, without waiting for all the computing unit groups, and the second computing device can participate in the global training of the federated learning model in time, which is beneficial to improve the efficiency of federated learning.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technologies, and in particular, to a federated learning method, apparatus, computing device, and storage medium. Background technique [0002] With the development of computer technology and the advancement of artificial intelligence technology, federated learning has gradually become a hot topic. Federated learning completes the training of machine learning and deep learning models through multi-party collaboration, which solves data silos while protecting user privacy and data security. The problem. [0003] Federated learning includes horizontal federated learning. In horizontal federated learning, multiple computing devices use their own sample data to jointly train the same model. However, due to the different training speeds of each computing device, a computing device with a slower training speed will lower the overall training efficiency, making it difficult to support...

AI Technical Summary

Problems solved by technology

[0003] Federated learning includes horizontal federated learning. In horizontal federated learning, multiple computing devices use their own sample data to jointly train the same model. However, due to the different training speeds of each computing device, the slower computing device will slow down the overall performance. The training efficiency makes it difficult to support efficient horizontal federated learning

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