Model learning method, device and system based on private data protection

Abstract

The embodiment of the invention provides a model learning method, device and system based on private data protection. The method comprises the step that non-centralized computing platform member objects in the alliance send model parameters of a learning model which is trained locally to centralized computing platform member objects in the alliance; model parameters of learning models of at leasttwo member objects are centrally calculated in the alliance obtained by the platform member objects through multi-party security calculation, and determining target model parameters; the centralized computing platform member objects send the target model parameters to non-centralized computing platform member objects in the alliance; and the member objects of the non-centralized computing platformin the alliance adjust the local learning model based on the target model parameters.

Description

technical field [0001] This document relates to the technical field of data processing, in particular to a model learning method, device and system based on private data protection. Background technique [0002] With the development of artificial intelligence, deep learning models have been used more and more widely due to their ability to process information mechanized. In the financial field, deep learning models can analyze financial data to understand users more deeply, and are suitable for application scenarios such as financial marketing and financial risk control. At present, small and medium-sized financial institutions are unable to build their own deep learning models to improve services. There are two main reasons for this problem: First, small and medium-sized financial institutions have limited investment costs and do not have the budget to build and maintain models. Second, financial data is heavily skewed in large financial institutions. Due to the sensitiv...

AI Technical Summary

Problems solved by technology

At present, small and medium-sized financial institutions are unable to build their own deep learning models to improve services. There are two main reasons for this problem: First, small and medium-sized financial institutions have limited investment costs and do not have the budget to build and maintain models

Second, financial data is heavily skewed in large financial institutions. Due to data sensitivity, financial data is not easy to share, resulting in small and medium-sized financial institutions being unable to accumulate sufficient financial data to train models.

Images