A training method and device for a federated learning model

Abstract

The present invention relates to the field of artificial intelligence, and discloses a federated learning model training method and device, wherein the method includes: before the server issues the global parameters of the k+1th training cycle, according to the connection status of the terminals participating in the federated learning model, Determining the first terminal and the second terminal; the first terminal is a terminal that cannot be connected; the second terminal is a terminal that is normally connected; the server determines the second local parameters of the M network layers whose contribution meets the set condition in the first terminal; According to the second local parameters of the M network layers of the first terminal in the kth training cycle and the first local parameters of the N network layers uploaded by the second terminal, the server determines that the k+1th training cycle is sent to the second The global parameters of the N network layers of the terminal are used to reduce the impact on the training effect of the model due to the terminal being offline.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a training method and device for a federated learning model. Background technique [0002] With the development of computer technology, more and more technologies are applied in the financial field. The traditional financial industry is gradually transforming into technology finance (Fintech). The technology of the federated learning model is no exception. However, due to the security and real-time nature of the financial industry Requirements, but also higher requirements for technology. [0003] In the field of deep learning technology, users can obtain deep learning network models with better prediction effects through training models. However, deep learning network models with better prediction effects usually have relatively complex network structures, thus occupying a large storage space. Correspondingly, when using the deep learning network model to predict the data...

AI Technical Summary

Problems solved by technology

However, in the current federated learning product scenario, due to the influence of device bandwidth, network and other factors, devices participating in federated training often go offline

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