Controlled shared learning method and system based on federated learning

Abstract

The invention provides a controlled shared learning method and system based on federated learning, and the method comprises the steps: receiving the parameters of all client models Vi,t, selecting oneor more shared clients from all clients according to the parameters, and transmitting a sharing instruction to a shared client, wherein Vi,0 is obtained through calculation power construction of eachclient and training according to a corresponding private data set, and classification module structures of all the models Vi,0 are consistent; and obtaining a classification module parameter St of the shared client model Vi,t, and sending the calculated classification module parameter St + 1 to each client, so that each client obtains the model Vi,t + 1 according to the private data set and the classification module parameter St + 1. According to the method, only part of model parameters need to be provided to complete shared learning, so that the data security is well guaranteed; and whethereach client model participates in shared learning and the like is autonomously controllable.

Description

technical field [0001] The invention relates to the field of computer software, in particular to a controlled shared learning method and system based on federated learning. Background technique [0002] Data is the cornerstone of artificial intelligence technology. With the rapid development of artificial intelligence technology, data security and privacy issues have attracted widespread attention. Due to issues such as data privacy and communication security, deep learning models cannot make full use of these data. Therefore, in order to solve such problems, people have proposed distributed machine learning methods such as federated learning and shared learning. These methods enable the model to effectively learn the local data of each client without directly exposing the local data. [0003] Federated learning refers to a method that performs machine learning on multiple independent clients and combines client model gradients on the server. Federated learning is a metho...

AI Technical Summary

Problems solved by technology

[0011] In order to solve the above problems, the purpose of the present invention is to provide a controlled shared learning method and system based on federated learning, which aims to reduce the communication cost in federated learning, ensure that local data will not be leaked by the model during the communication process, and the client Controllably customize the model to solve the technical problems of current federated learning application scenarios.

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