Collaborative model training task configuration method for intelligent edge computing

Abstract

The invention discloses a collaborative model training task configuration method for intelligent edge computing, the method is used for an edge computing node and comprises one or more training time slots, and each training time slot comprises the following steps: sending a model training request to one or more mobile devices; receiving an available state and a user data scale of the current timeslot reported by one or more mobile devices; based on the previously obtained task configuration result and the current available state of each mobile device, determining the number of small trainingwheels required by the mobile devices participating in training and the interactive model training; and performing interactive model training with the mobile devices participating in training until the determined number of small training rounds is reached, constructing an optimization problem with the purpose of minimizing the use of edge training resources according to the training effect and theuser data scale reported by each mobile device, and solving the optimization problem to obtain a new task configuration result. Compared with other methods, the training resource consumption is muchless, and the precision difference is small.

Description

Technical field [0001] The present invention relates to a synergistic model training task configuration method, which relates to a synergistic model training task configuration method for intelligent edge calculation. Background technique [0002] A large number of user data is generated in the user using mobile devices, such as mobile phones, tablets, etc., including browsing records, typing records, and various types of log information. These data can help service providers for better service deployment and availability after being analyzed. This type of analysis processing means often by means of a machine learning model. Specifically, a machine learning model includes model structure and model parameters, and the accuracy of the machine learning model reflected on a particular data set, if the classification model is classified, the correct classification ratio is obtained. As the accuracy of the model. Then, the goal of the service provider is to use the user data generated ...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: In order to overcome the deficiencies in the prior art, on the one hand, the present invention provides a collaborative model training task configuration method for intelligent edge computing to solve the problem that distributed machine learning training data is difficult to share, and while ensuring accuracy case, try to save resource consumption

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