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公开(公告)号：US12056582B2
公开(公告)日：2024-08-06
申请号：US16921207
申请日：2020-07-06
Inventor： Bing Ren , Shengwen Yang , Xuhui Zhou
CPC classification number: G06N20/00 , G06F21/602 , H04L9/008 , H04L9/0825 , H04L9/3073 , G06F2221/2107
Abstract: A method and device for training a model based on federated learning are provided. The method includes: receiving a second original independent variable calculated value from a second data provider device; the second original independent variable calculated value being calculated by the second data provider device according to a second original independent variable and a second model parameter; calculating a dependent variable estimation value according to a first model parameter initial value of a first provider device, a first original independent variable of the first data provider device, and the second original independent variable calculated value; calculating a difference between a dependent variable of the first data provider device and the dependent variable estimation value; calculating a gradient of a loss function with respect to a first model parameter, according to the difference; and updating the first model parameter according to the gradient of the loss function with respect to the first model parameter.
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公开(公告)号：US11368855B2
公开(公告)日：2022-06-21
申请号：US16945834
申请日：2020-08-01
Inventor： Shengwen Yang , Wenwu Zhu , Mingyang Dai
Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.
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公开(公告)号：US20210004718A1
公开(公告)日：2021-01-07
申请号：US16921207
申请日：2020-07-06
Inventor： Bing Ren , Shengwen Yang , Xuhui Zhou
Abstract: A method and device for training a model based on federated learning are provided. The method includes: receiving a second original independent variable calculated value from a second data provider device; the second original independent variable calculated value being calculated by the second data provider device according to a second original independent variable and a second model parameter; calculating a dependent variable estimation value according to a first model parameter initial value of a first provider device, a first original independent variable of the first data provider device, and the second original independent variable calculated value; calculating a difference between a dependent variable of the first data provider device and the dependent variable estimation value; calculating a gradient of a loss function with respect to a first model parameter, according to the difference; and updating the first model parameter according to the gradient of the loss function with respect to the first model parameter.
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公开(公告)号：US20210076224A1
公开(公告)日：2021-03-11
申请号：US16945834
申请日：2020-08-01
Inventor： Shengwen Yang , Wenwu Zhu , Mingyang Dai
Abstract: A network convergence method and device, an electronic apparatus, a storage medium are provided. The method includes: taking online information obtained at least from network data as first nodes, and constructing a first network with the first nodes based on an association among the online information, the online information including at least one account and at least one piece of network media information; taking offline information obtained at least from mobile communication data as second nodes, and constructing a second network with the second nodes based on an association among the offline information, the offline information including at least one terminal identifier and location information of a terminal corresponding to the at least one terminal identifier; and converging the first network and the second network by using an association between the account in the online information and the terminal identifier in the offline information, to construct a heterogeneous network.
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