Transverse federation learning system optimization method and device, equipment and readable storage medium

Abstract

The invention discloses a transverse federated learning system optimization method and device, equipment and a readable storage medium. The method comprises the steps that a target type of local modelparameter updating needing to be sent by each participating device in each round of model updating is determined from parameter updating types according to a preset strategy, and the parameter updating types comprise model parameter information and gradient information; sending indication information for indicating the target type to each participation device, so that each participation device performs local training according to the indication information and returns local model parameter update of the target type; and fusing the local model parameter updates of the target type received fromeach participating device, and sending the global model parameter updates obtained by fusion to each participating device, so that each participating device performs model updating according to the global model parameter updates. According to the method, the advantages of a gradient averaging algorithm and a model averaging algorithm are combined, and a hybrid federated averaging mechanism is realized.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a horizontal federated learning system optimization method, device, equipment and readable 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. [0003] Horizontal federated learning, also known as feature-aligned federated learning, is to take out the participants when the data features of each participant overlap more (that is, the data features are aligned) and the users overlap less. Joint machine learning is performed on the part of the data that has the same characteristics as the previous data but not exactly the same users. [0004] The ...

AI Technical Summary

Problems solved by technology

[0005] The main purpose of the present invention is to provide a horizontal federated learning system optimization method, device, equipment and readable storage medium, aiming to solve the problem that the existing horizontal federated learning scheme cannot take into account both communication overhead and model performance

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