Federal model training method and device, customer portraying method and device, equipment and medium

Abstract

The invention relates to the technical field of user portraying, and provides a federation model training method and device, a customer portraying method and device, equipment and a medium, and the method comprises the steps: obtaining a participant list and an initial customer portraying federation model, and screening out qualified participants from the participant list according to a preset screening scheme; sending the initial customer portrait federation model to each qualified participant; receiving returned model parameters; performing abnormal feature extraction through a malicious parameter detection model by applying an MPI parallel method, and outputting an identification result of each model parameter according to the extracted abnormal features; filtering malicious parameters to obtain final normal parameters; and performing updating and federation learning to obtain a global customer portrait federation model. According to the method, the MPI parallel method is applied, abnormal feature extraction and malicious parameter filtering processing are carried out through the malicious parameter detection model, malicious parameters provided by malicious participants are automatically removed, and the efficiency and precision of federal learning modeling are improved.

Description

technical field [0001] The present invention relates to the technical field of user portraits, in particular to a federated model training, customer portrait method, device, computer equipment and storage medium. Background technique [0002] With the increasing popularity of machine learning, big data-driven intelligent applications will soon be applied to all aspects of our daily lives, including intelligent voice, medical care, transportation and more. However, in traditional machine learning methods, the key to ensuring the accuracy of the training model is to collect a sufficient amount of data, which may contain private information about individuals, such as personal medical information or personal itinerary information. Public concerns about the compromise of personal privacy. Recently, federated learning has been more and more widely used because of its significant advantages in privacy protection. By directly aggregating the model parameters of each participant, th...

AI Technical Summary

Problems solved by technology

However, in traditional machine learning methods, the key to ensuring the accuracy of the training model is to collect a sufficient amount of data, which may contain private information about individuals, such as personal medical information or personal itinerary information. Public concerns about compromised privacy

Recently, federated learning has been more and more widely used because of its significant advantages in privacy protection. By directly aggregating the model parameters of each participant, the model parameters obtained by the aggregation are used to train the global federated model, and then the training The final global model parameters are then fed back to each participant for each participant to update the local model. However, if there is a malicious participant in each participant and the malicious participant provides a false or malicious model during the local training process Parameters will lead to lower effectiveness of global model parameters, which will directly affect the overall federated model quality, leading to the failure of the entire federated learning process, which will lead to lower efficiency and accuracy of federated learning modeling

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