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 edge computing nodes and includes one or more training time slots, and each training time slot includes the following steps: moving to one or more The device sends a model training request; receives the available status and user data scale of the current time slot reported from one or more mobile devices; The number of training rounds required for interactive model training; interactive model training is carried out with the mobile devices participating in the training until the number of training rounds is reached. According to the training effect and the scale of user data reported by each mobile device, it is constructed to minimize the edge training resources The use-targeted optimization problem is solved and a new task configuration result is obtained. Compared with other methods, the training resource consumption of the present invention is much less, and the accuracy is not much different.

Description

technical field [0001] The invention relates to a collaborative model training task configuration method, in particular to a collaborative model training task configuration method oriented to intelligent edge computing. Background technique [0002] When users use mobile devices, such as mobile phones and tablet computers, a large amount of user data will be generated, including browsing records, typing records, and various log information. After the data is analyzed and processed, it can help service providers to deploy and provide better services. Such analytical processing methods often rely on machine learning models. Specifically, a machine learning model includes model structure and model parameters, as well as the accuracy of the machine learning model on a specific data set, such as using a classification model to classify a data set, and the correct classification ratio obtained as model accuracy. Then, the goal of the service provider is to use the distributed u...

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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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