Machine learning model training method, device and system, equipment and storage medium

Abstract

The invention provides a machine learning model training method, device and system, electronic equipment and a computer readable storage medium. The method comprises the steps that service party equipment sends a machine learning model to multiple pieces of training participant equipment, so that the multiple pieces of training participant equipment independently train the machine learning model based on training samples stored in the multiple pieces of training participant equipment, wherein the first loss function used by the training participant equipment for training the machine learning model is used for balancing the distribution of parameters of the machine learning model after the training participant equipment is trained; receiving training results returned by the plurality of training participant devices; aggregating the training results returned by the plurality of training participant devices to obtain a global machine learning model; and training the global machine learning model according to the training sample stored in the service side device. According to the method and the device, the prediction stability of distributed learning can be realized on the basis of realizing intensive utilization of resources for training the machine learning model.

Description

technical field [0001] The present application relates to artificial intelligence technology, and in particular to a machine learning model training method, device, system, electronic equipment, and computer-readable storage medium. Background technique [0002] Artificial Intelligence (AI) involves a wide range of fields and plays an increasingly important role. As a subset of artificial intelligence technology, machine learning has made breakthroughs in many application fields, especially in the financial field. It can predict user credit based on limited user data, thereby providing an important basis for the development of related businesses to avoid Financial risk. [0003] Related technologies provide a distributed learning solution to solve the problem that a single device is difficult to meet the storage space and computing power requirements for training large-scale machine learning models, so as to achieve intensive utilization of resources. In particular, federat...

AI Technical Summary

Problems solved by technology

[0003] Related technologies provide a distributed learning solution to solve the problem that a single device is difficult to meet the storage space and computing power requirements for training large-scale machine learning models, so as to achieve intensive utilization of resources. In particular, federated learning is a distributed learning technology subset, which can further provide data privacy protection function

[0004] However, in the process of implementing the embodiment of the present application, the applicant found that the devices participating in the distributed learning each train the machine learning model based on the training data held for resource saving and data security requirements. Due to the distribution of training data The difference of different participants leads to the situation that the prediction results of the machine learning model for the data of different participants are very different, that is, the prediction stability of the machine learning model cannot meet the practical needs, thus, the resource saving requirements of the distributed learning equipment and the machine learning model There is a contradiction between the predictive stability of the

Images