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

Abstract

The invention discloses a transverse federated learning system optimization method and device, and a readable storage medium. The method comprises the steps of randomly determining a neuron on-off mode of a to-be-trained neural network model; sending the neuron on-off mode to each participating device, so that each participating device carries out on-off processing on neurons in respective local neural network models according to the neuron on-off mode, and carries out local training on the processed neural network models to obtain local model parameter updates; and fusing the local model parameter updates, and sending global model parameter updates obtained by fusion to the participation devices, so that the participation devices perform model updating on the local neural network models according to the global model parameter updates. Compared with an existing scheme for avoiding an over-fitting phenomenon, the strategy for randomly selecting neuron closing adopted in the method can be well combined with federated learning, and excessive extra time cost and computing resource consumption cannot be brought.

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 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. 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. [0003] In practical application...

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Problems solved by technology

[0005] The main purpose of the present invention is to provide a horizontal federated learning system optimization method, equipment and readable storage medium, aiming at solving the problem that existing solutions for avoiding overfitting phenomenon are applied to federated learning scenarios, the time cost of model training is high, and the computing resources Problems with high consumption

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