Longitudinal federal modeling method based on LightGBM algorithm

Abstract

The invention relates to the related field of machine learning of privacy protection, in particular to a vertical federated modeling method based on a LightGBM algorithm, which comprises vertical federated modeling preparation work, initiator P1 data preparation work, partner P0 data preparation work, model training work and model evaluation work. The invention provides a novel longitudinal federated learning system structure based on a tree model, the two parties are allowed to construct a joint training model on the premise of protecting data privacy, and the two parties are enabled to train the joint model without the help of a trusted coordinator, so that a gradient value is protected, and the security of a protocol is improved; the architecture of the method is easy to expand, and besides two-party model training, the architecture of the method supports multi-party joint modeling; and according to the longitudinal federation learning based on the lightGBM algorithm, safety, speed, accuracy, support category features and continuous features are comprehensively considered, a large amount of training data can be processed under the same data set and features, and the method is suitable for engineering.

Description

technical field [0001] The invention relates to the field of machine learning related to privacy protection, in particular to a longitudinal federated modeling method based on the LightGBM algorithm. Background technique [0002] Machine learning (Machine Learning, referred to as ML) refers to the process of using certain algorithms to guide computers to use known data to independently build reasonable models, and use this model to give judgments on new situations. , Mechanical failure prediction, insurance pricing, 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 Gene...

AI Technical Summary

Problems solved by technology

[0006] In the existing LightGBM model algorithm in federated learning, the existing difficulties and problems are: how to ensure the data privacy and at the same time ensure the accuracy of the comparison between the joint modeling results and the plaintext results, how to carry out the safe interaction of the joint modeling data, how to ensure The safe transfer of model parameters in the modeling process, in view of these problems, this invention proposes a longitudinal federated modeling method based on the LightGBM algorithm

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