Cross-feature federal learning method and prediction method based on Soft GBDT
A learning method and federated technology, applied in the field of machine learning technology, can solve the problems of slow model training speed and low training efficiency, and achieve the effects of avoiding heavy data processing burden, improving training efficiency, and ensuring security
Active Publication Date: 2021-09-24
TONGDUN HLDG CO LTD +1
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[0007] The purpose of this disclosure is to provide a Soft GBDT-based cross-feature federated learning method, a SoftGBDT-based cross-feature federated learning device, a Soft GBDT-based cross-feature federated prediction method, a Soft GBDT-based cross-feature federated prediction device, computer-readable Storage media and electronic equipment, and then at least to a certain extent overcome the problems of slow model training and low training efficiency due to limitations and defects of related technologies
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[0066] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided in order to give a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details being omitted, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known technical solution...
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The invention relates to a cross-feature federated learning method and device based on Soft GBDT, and a data prediction method and device, and relates to the technical field of machine learning, the cross-feature federated learning method based on Soft GBDT comprises the following steps: calculating a first linear regression part of first feature data in a first inner node in Soft GBDT by using Soft GBDT; calculating output values of leaf nodes of the first linear regression part and the second linear regression part in the Soft GBDT, and calculating a local loss function of a current soft decision tree included in the Soft GBDT; and calculating a global loss function of the Soft GBDT according to the local loss function, and calculating a first gradient of the first inner node according to the global loss function so as to update parameters of the first inner node of the Soft GBDT. The model training speed can be increased, and the model training efficiency can be improved.
Description
technical field [0001] The embodiments of the present disclosure relate to the field of machine learning technology, in particular, to a Soft GBDT-based cross-feature federated learning method, a Soft GBDT-based cross-feature federated learning device, a Soft GBDT-based cross-feature federated prediction method, and a Soft GBDT-based cross-feature federated learning method. A cross-feature federation prediction device, a computer-readable storage medium, and an electronic device. Background technique [0002] The cross-feature scenario in privacy-preserving machine learning belongs to the cross-feature federation in the theoretical system of knowledge federation. It means that the training or inference samples used by multiple institutions are consistent, but the characteristics are different. Only one institution holds the label, training and Reasoning can be done with the assistance of multiple parties. [0003] In the current machine learning based on cross-feature scena...
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Login to View More IPC IPC(8): G06K9/62G06N20/00
CPCG06N20/00G06F18/24323G06F18/214
Inventor周一竞孟丹李宏宇李晓林
OwnerTONGDUN HLDG CO LTD