Federated learning model training method and device

Abstract

The invention relates to the field of artificial intelligence, and discloses a federal learning model training method and device, and the method comprises the steps that a server determines a first terminal and a second terminal according to the connection state of terminals participating in a federal learning model before issuing global parameters of a (k+1)th training period; wherein the first terminal is a terminal which cannot be connected; wherein the second terminal is a normally connected terminal; the server determines second local parameters of M network layers with contribution degrees meeting set conditions in the first terminal; according to the second local parameters of the M network layers of the first terminal in the kth training period and the first local parameters of the N network layers uploaded by the second terminal, the server sends the first local parameters to the kth training period, and determines the global parameters of the N network layers issued to the second terminal in the (k+1)th training period, so as to reduce the influence on the training effect of the model caused by offline of the terminal.

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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