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公开(公告)号：US20230237333A1
公开(公告)日：2023-07-27
申请号：US18185550
申请日：2023-03-17
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yunfeng SHAO , Shaoming SONG , Wenpeng LI , Kaiyang GUO , Li QIAN
IPC: G06N3/08
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.
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公开(公告)号：US20250068921A1
公开(公告)日：2025-02-27
申请号：US18944331
申请日：2024-11-12
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yinchuan LI , Yunfeng SHAO , Wenqian LI
Abstract: A causality determining method relates to the field of artificial intelligence. The method includes: obtaining first information that is obtained by predicting a plurality of variables by a generative flow model and that indicates causality between the plurality of variables; and predicting second information of the plurality of variables based on the first information and by using the generative flow model, where the second information indicates that first causality exists between a first variable and a second variable in the plurality of variables, and the first information indicates that the first causality does not exist between the first variable and the second variable. This reduces computing capability overheads and improves a convergence speed of the model.
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3.发明申请公开(公告)号：US20230082173A1
公开(公告)日：2023-03-16
申请号：US17989777
申请日：2022-11-18
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Gang LI , Yunfeng SHAO , Lei ZHANG
Abstract: The technology of this application relates to a training method that includes a first terminal obtaining a to-be-trained first machine learning model from the server. The first terminal is any one of a plurality of terminals. The first terminal trains the first machine learning model by using local data stored by the first terminal, to obtain trained model parameters. The first terminal determines, based on a collaboration relationship, a first collaborative terminal corresponding to the first terminal, and sends a part or all of the trained model parameters of the first terminal to the server by using the first collaborative terminal. The collaboration relationship is delivered by the server to the first terminal. The foregoing manner can improve security of data exchange between the server and the terminal.
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Patent Agency Ranking
公开(公告)号：US20200302216A1
公开(公告)日：2020-09-24
申请号：US16894425
申请日：2020-06-05
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Ke HE , Zhitang CHEN , Yunfeng SHAO
Abstract: This application provides a data stream identification method and apparatus and belongs to the field of Internet technologies. The method includes: obtaining packet transmission attribute information of N consecutive packets in a target data stream; generating feature images of the packet transmission attribute information of the N consecutive packets based on the packet transmission attribute information of the N consecutive packets; and inputting the feature images into a pre-trained image classification model, to obtain a target application identifier corresponding to the target data stream. According to this application, accuracy of identifying an application identifier corresponding to a data stream can be improved.
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公开(公告)号：US20180260739A1
公开(公告)日：2018-09-13
申请号：US15980866
申请日：2018-05-16
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yunfeng SHAO , Jun XU , Masood MORTAZAVI
Abstract: The method and apparatus that are applied to a machine learning system which includes at least one parameter collection group and at least one parameter delivery group. Each parameter collection group is corresponding to at least one parameter delivery group. The method includes: when any parameter collection group meets an intra-group combination condition, combining model parameters of M nodes in the parameter collection group to obtain a first model parameter of the parameter collection group, where a smallest quantity s of combination nodes in the parameter collection group≤M≤a total quantity of nodes included in the parameter collection group; and sending the first model parameter of the parameter collection group to N nodes in a parameter delivery group corresponding to the parameter collection group, where 1≤N≤a total quantity of nodes included in the parameter delivery group corresponding to the parameter collection group.
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公开(公告)号：US20240086720A1
公开(公告)日：2024-03-14
申请号：US18518753
申请日：2023-11-24
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Yinchuan LI , Yunfeng SHAO , Li QIAN
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: This application provides a federated learning method, apparatus, and system, so that a server retrains a received model in a federated learning process to implement depersonalization processing to some extent, to obtain a model with higher output precision. The method includes: First, a first server receives information about at least one first model sent by at least one downstream device, where the at least one downstream device may include another server or a client connected to the first server; the first server trains the at least one first model to obtain at least one trained first model; and then the first server aggregates the at least one trained first model, and updates a locally stored second model by using an aggregation result, to obtain an updated second model.
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公开(公告)号：US20230325722A1
公开(公告)日：2023-10-12
申请号：US18327952
申请日：2023-06-02
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： De-Chuan ZHAN , Xinchun LI , Shaoming SONG , Yunfeng SHAO , Bingshuai LI , Li QIAN
IPC: G06N20/00
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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公开(公告)号：US20190317218A1
公开(公告)日：2019-10-17
申请号：US16456057
申请日：2019-06-28
Applicant: HUAWEI TECHNOLOGIES CO., LTD.
Inventor： Tongtong CAO , Yunfeng SHAO , Jun YAO
Abstract: A ground environment detection method and apparatus are disclosed, where the method includes: scanning a ground environment by using laser sounding signals having different operating wavelengths, receiving a reflected signal that is reflected back by the ground environment, determining scanning spot information of each scanning spot of the ground environment based on the reflected signal, determining space coordinate information and a laser reflection feature of each scanning spot based on each piece of scanning spot information, partitioning the ground environment into sub-regions having different laser reflection features, and determining a ground environment type of each sub-region. Lasers having different operating wavelengths are used to scan the ground, and the ground environment type is determined based on the reflection intensity of the ground environment under different wavelengths of lasers, thereby improving a perception effect of a complex ground environment, and better determining a passable road surface.
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