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公开(公告)号:US20230342669A1
公开(公告)日:2023-10-26
申请号:US18344188
申请日:2023-06-29
Applicant: Huawei Technologies Co., Ltd.
Inventor: Yunfeng Shao , Bingshuai Li , Jun Wu , Haibo Tian
Abstract: Embodiments of this application provide a machine learning model update method, applied to the field of artificial intelligence. The method includes: A first apparatus generates a first intermediate result based on a first data subset. The first apparatus receives an encrypted second intermediate result sent by a second apparatus, where the second intermediate result is generated based on a second data subset corresponding to the second apparatus. The first apparatus obtains a first gradient of a first model, where the first gradient of the first model is generated based on the first intermediate result and the encrypted second intermediate result. After being decrypted by using a second private key, the first gradient of the first model is for updating the first model, where the second private key is a decryption key generated by the second apparatus for homomorphic encryption.
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公开(公告)号:US20230353347A1
公开(公告)日:2023-11-02
申请号:US18344185
申请日:2023-06-29
Applicant: Huawei Technologies Co., Ltd.
Inventor: Yunfeng Shao , Bingshuai Li , Haibo Tian
CPC classification number: H04L9/0836 , H04L9/008
Abstract: A first apparatus provides a second apparatus with encrypted label distribution information for the first node, so that the second apparatus calculates an intermediate parameter of a segmentation policy of the second apparatus side based on the encrypted label distribution information, and therefore a gain of the segmentation policy of the second apparatus side can be obtained. A preferred segmentation policy of the first node can also be obtained based on the gain of the segmentation policy of the second apparatus side and a gain of a segmentation policy of the first apparatus side. The encrypted label distribution information includes label data and distribution information, and is in a ciphertext state. The encrypted label distribution information can be used to determine the gain of the segmentation policy without leaking a distribution status of a sample set on the first node.
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