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

A cloud server and equipment technology, applied in the field of neural network and federated learning, can solve the problems of poor neural network performance, long average execution time, low prediction accuracy, etc., achieve good performance, improve prediction accuracy and average inference execution time, the effect of protecting data privacy

Active Publication Date: 2020-09-04
EAST CHINA NORMAL UNIV
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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.

Method used

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

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Experimental program
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Embodiment 1

[0039] See attached Figure 4 , the present invention adopts the joint learning framework of cloud server and IoT device collaboration of distributed AI system architecture, and its joint learning specifically includes the following steps:

[0040] Step 1: Design the neural network model

[0041] Design a suitable BranchyNet neural network model according to the computing and storage resources of cloud servers and terminal IoT (Internet of Things, Internet of Things) devices. The Branch part of the cloud, the Trunk and Branch part.

[0042] Step 2: Cloud offline training

[0043] The BranchyNet model is trained on the cloud with public datasets, with the purpose of pre-training to obtain the weight of the initial BranchyNet neural network model.

[0044] The training method of the present invention is described below by taking cross entropy (cross entropy) as the loss function of the neural network as an example. The cloud offline training in the first stage is a joint opt...

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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...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/50G06N3/063G06N3/04G06N3/08H04L29/08
CPCG06F9/5072G06N3/063G06N3/084H04L67/12G06N3/045
Inventor 陈铭松张心潜
Owner EAST CHINA NORMAL UNIV
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