Longitudinal federated learning system optimization method, apparatus and device and readable storage medium

Abstract

The invention discloses a longitudinal federated learning system optimization method, apparatus and device and a readable storage medium. The longitudinal federated learning system optimization methodcomprises the steps: obtaining a sample alignment result obtained by conducting sample alignment on local training sample sets of all participation devices, wherein data characteristics of samples owned by all the participation devices are not completely the same; according to the sample alignment result, cooperating with each participation device to obtain multiple groups of input data with different data dimensions; and training a preset to-be-trained machine learning model with variable input data feature dimensions according to the multiple groups of input data to obtain a target machinelearning model. According to the longitudinal federated learning system optimization method, when a participant of longitudinal federated learning uses the model trained through longitudinal federatedlearning, the participant can use the model independently without cooperation of other participants, so that the application range of longitudinal federated learning is expanded.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a longitudinal federated learning system optimization method, device, equipment and readable storage medium. Background technique [0002] With the development of artificial intelligence, in order to solve the problem of data islands, people put forward the concept of "federated learning", so that both sides of the federation can also conduct model training to obtain model parameters without giving their own data, and can avoid data The issue of privacy breaches. [0003] Vertical federated learning is to take out the part of users and data with the same participant users but different user data characteristics to jointly train the machine learning model when the data characteristics of the participants are small and the users overlap a lot. For example, there are two participants A and B belonging to the same region, where participant A is a bank and participan...

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Problems solved by technology

[0004] However, at present, when using a model trained through longitudinal federated learning, it is necessary to combine the data of all participants participating in federated learning to complete the prediction task. However, the real scenario may be that a participant needs to make predictions on its local users , but other participants do not have the user's data, or the participant cannot obtain the data of other participants due to reasons such as cancellation of cooperation, so that the participant cannot use the trained model to predict the user, resulting in There are limitations in the scope of the trained model

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