Method and device for selecting terminal equipment nodes in federated learning system

Abstract

The invention provides a method and a device for selecting terminal equipment nodes in a federated learning system. The method comprises the following steps: determining a test accuracy optimization target model selected corresponding to a terminal device node; and obtaining current environment state data corresponding to each federated learning iteration process, and inputting the current environment state data into a terminal equipment node selection model to obtain a terminal equipment node selection strategy corresponding to test accuracy and time delay constraints output by the terminal equipment node selection model. By adopting the method for selecting the terminal equipment nodes in the federated learning system, the federated learning performance can be ensured, the joint optimization of the resource utilization rate and the energy consumption of the terminal equipment is realized, the terminal equipment set is reasonably selected to participate in the aggregation of the federated learning model, and the efficiency and the accuracy of the federated learning are effectively improved.

Description

technical field [0001] The invention relates to the field of computer application technology, in particular to a method and device for selecting terminal equipment nodes in a federated learning system. In addition, it also relates to an electronic device and a non-transitory computer-readable storage medium. Background technique [0002] In recent years, with the massive use of mobile IoT devices, more and more machine learning applications have been popularized at the edge of the network. The traditional method of uploading raw data to a centralized server for model training has disadvantages such as high transmission delay and leakage of user privacy. In order to solve the above problems, a distributed model training architecture based on federated learning came into being. In this mode, the terminal device can use its own data to complete the training task locally, and then send the model parameters to the server for model aggregation. Since the size of the uploaded mo...

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Problems solved by technology

[0004] For this reason, the present invention provides a method and device for selecting terminal equipment nodes in a federated learning system to solve the problem in the prior art that only focuses on federated learning itself, without considering the resources of terminal equipment and equipment energy consumption in the training process, etc. Factors that lead to poor efficiency and accuracy of federated learning

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