Method for carrying out batch standardization in logic regression model of security federated learning

A logistic regression model, federated technology, applied in computing models, machine learning, computer security devices, etc., can solve problems such as model gradient increase, model parameter and input feature product sum limitation, and inability to converge, etc., to improve security, The effect of improving the success rate of training

CN113591115APending Publication Date: 2021-11-02神谱科技(上海)有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2021-11-02

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Abstract

The invention relates to the related field of federated learning, and particularly discloses a method for carrying out batch standardization in a logic regression model of security federated learning, which comprises the following steps: S1, federated learning participants prepare training data: the federated learning participants can have multiple parties P1, P2,..., Pn, each participant has training data data_1, data_2,..., data_n, one party has a label y, it is assumed that the participant with a label is P1, and in addition, a coordinator is used for gradient decryption; S2, data set fusion is performed: in the longitudinal federated learning, intersection is firstly carried out on the data, and a sample ID (Identity) commonly owned by each participant is screened to form a fused data set; S3, a logistic regression model in longitudinal federated learning is trained; S4, prediction is performed. According to the method, on one hand, model training failure caused by incapability of convergence due to overlarge gradient in logistic regression algorithm modeling of security federated learning can be avoided, and the training success rate of the model is improved;.
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Description

technical field

[0001] The invention relates to the field related to federated learning, in particular to a method for performing batch normalization in a logistic regression model of safe federated learning. Background technique

[0002] Machine learning refers to the process of using certain algorithms to guide the computer to independently construct a reasonable model using known data, and using this model to make judgments on new situations. , financial risk management and other applications play a very important role. Traditionally, machine learning models are trained on a centralized corpus of data, which may be collected by a single or multiple data providers. Although parallel distributed algorithms have been developed to speed up the training process, the training data itself is still collected and stored centrally in a single data center.

[0003] In May 2018, the European Union passed the General Data Protection Regulation (GDPR) bill, raising the requirements f...

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Embodiment Construction

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0040] see figure 1 , the present invention provides a technical solution: a method for performing batch normalization in a logistic regression model of security federated learning, comprising the following steps:

[0041] S1. Participants in federated learning prepare training data: There can be multiple parties P1, P2, ..., Pn for federated learning participants, and each participant has training data data_1, data_2, ..., data_n, of which One party has the ...