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    QUANTUM DEFORMED BINARY NEURAL NETWORKS
    16.
    发明申请
    QUANTUM DEFORMED BINARY NEURAL NETWORKS 审中-公开

    公开(公告)号：WO2022072869A1

    公开(公告)日：2022-04-07

    申请号：PCT/US2021/053226

    申请日：2021-10-01

    Applicant: QUALCOMM INCORPORATED

    Inventor： BONDESAN, Roberto ,  WELLING, Max

    IPC: G06N3/08 ,  G06N10/60 ,  G06N3/04

    Abstract: Certain aspects of the present disclosure provide techniques for processing data in a quantum deformed binary neural network, including: determining an input state for a layer of the quantum deformed binary neural network; computing a mean and variance for one or more observables in the layer; and returning an output activation probability based on the mean and variance for the one or more observables in the layer.

    SPARSITY-INDUCING FEDERATED MACHINE LEARNING
    17.
    发明申请
    SPARSITY-INDUCING FEDERATED MACHINE LEARNING 审中-公开

    公开(公告)号：WO2022067355A1

    公开(公告)日：2022-03-31

    申请号：PCT/US2021/071633

    申请日：2021-09-28

    Applicant: QUALCOMM INCORPORATED

    Inventor： LOUIZOS, Christos ,  HOSSEINI, Hossein ,  REISSER, Matthias ,  WELLING, Max ,  SORIAGA, Joseph Binamira

    IPC: G06N3/04 ,  G06N3/08 ,  G06N7/00 ,  G06N20/00

    Abstract: Aspects described herein provide techniques for performing federated learning of a machine learning model, comprising: for each respective client of a plurality of clients and for each training round in a plurality of training rounds: generating a subset of model elements for the respective client based on sampling a gate probability distribution for each model element of a set of model elements for a global machine learning model; transmitting to the respective client: the subset of model elements; and a set of gate probabilities based on the sampling, wherein each gate probability of the set of gate probabilities is associated with one model element of the subset of model elements; receiving from each respective client of the plurality of clients a respective set of model updates; and updating the global machine learning model based on the respective set of model updates from each respective client of the plurality of clients.

    DIFFERENTIALLY PRIVATE ITERATIVELY REWEIGHTED LEAST SQUARES
    19.
    发明申请
    DIFFERENTIALLY PRIVATE ITERATIVELY REWEIGHTED LEAST SQUARES 审中-公开
    差别私密的，经过更新的最小平方

    公开(公告)号：WO2017190004A1

    公开(公告)日：2017-11-02

    申请号：PCT/US2017/030123

    申请日：2017-04-28

    Applicant: QUALCOMM INCORPORATED

    Inventor： PARK, Mijung ,  WELLING, Max

    IPC: G06F21/62 ,  G06F17/00 ,  G06N99/00

    Abstract: A method for privatizing an iteratively reweighted least squares (IRLS) solution includes perturbing a first moment of a dataset by adding noise and perturbing a second moment of the dataset by adding noise. The method also includes obtaining the IRLS solution based on the perturbed first moment and the perturbed second moment. The method further includes generating a differentially private output based on the IRLS solution.

    Abstract translation: 用于对迭代重新加权最小二乘（IRLS）解进行私有化的方法包括通过添加噪声来干扰数据集的第一时刻，并通过添加噪声扰动数据集的第二时刻。 该方法还包括基于受扰动的第一时刻和受扰动的第二时刻获得IRLS解。 该方法还包括基于IRLS解决方案生成差异私人输出。

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