MACHINE LEARNING MODEL TRAINING METHOD AND RELATED DEVICE

    公开(公告)号:US20230237333A1

    公开(公告)日:2023-07-27

    申请号:US18185550

    申请日:2023-03-17

    CPC classification number: G06N3/08

    Abstract: A machine learning model training method is applied to a first client, a plurality of clients are communicatively connected to a server, the server stores a plurality of modules, and the plurality of modules are configured to construct at least two machine learning models. The method includes: obtaining a first machine learning model, where at least one first machine learning model is selected based on a data feature of a first training data set stored in the first client; performing a training operation on the at least one first machine learning model by using the first data set, to obtain at least one trained first machine learning model; and sending at least one updated module to the server, where the updated module is used by the server to update weight parameters of the stored modules.

    MODEL TRAINING METHOD, DATA PROCESSING METHOD, AND APPARATUS

    公开(公告)号:US20230325722A1

    公开(公告)日:2023-10-12

    申请号:US18327952

    申请日:2023-06-02

    CPC classification number: G06N20/00

    Abstract: This application discloses a model training method, and relates to the field of artificial intelligence. The method provided in this application is applicable to a machine learning system. The machine learning system includes a server and at least two client side devices. The method includes: A first client side device receives a first shared model sent by the server; outputs a first prediction result for a data set through the first shared model; obtains a first loss value based on the first prediction result; outputs a second prediction result for the data set through a first private model of the first client side device; obtains a second loss value based on the second prediction result; and performs second combination processing on the first loss value and the second loss value to obtain a third loss value, where the third loss value is used to update the first private model.

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