Joint learning framework based on cooperation of cloud server and IoT equipment

Abstract

The invention discloses a joint learning framework based on cooperation of a cloud server and IoT equipment. The method is characterized in that a cloud server and IoT equipment collaborative joint learning framework of a distributed AI system architecture is adopted, and the method specifically comprises the steps of cloud offline training, IoT equipment and cloud collaborative online joint training, IoT equipment and cloud collaborative reasoning and the like. Compared with the prior art, the joint learning framework has the advantages that the method adapts to different IoT use environmentsor use preferences of users, better performance effects of the local equipment model and the cloud model are realized, prediction accuracy and average reasoning execution time of the neural network in the IoT equipment are effectively improved, data privacy of local equipment users is protected, and the method is especially suitable for distributed application scenes with diversified data distribution.

Description

technical field [0001] The invention relates to the technical field of neural network and federated learning, in particular to a BranchyNet-based cloud server and terminal IoT device group collaborative reasoning and joint learning framework. Background technique [0002] With the development of Internet of Things technology, the arrival of 5G communication and the increasing popularity of various IoT devices, the massive data generated by the IoT terminal in the future will soon exceed the data volume of the existing Internet. Edge and terminal intelligence are attracting more and more attention, and artificial intelligence technology can be effectively used to process data generated by massive devices. In recent years, the computing power of IoT devices has been continuously improved. In particular, AI chips dedicated to neural network computing have gradually become standard equipment for mid-to-high-end devices, and artificial intelligence has gradually migrated from the...

AI Technical Summary

Problems solved by technology

[0005] Existing technologies cannot enable IoT devices with low hardware costs to better use deep neural networks to provide intelligent services, and there are problems such as poor neural network performance, low prediction accuracy, and long average execution time.

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