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A mobile edge distributed machine learning system and method

A machine learning and distributed technology, applied in machine learning, instruments, resource allocation, etc., can solve the problems of large differences in computing and communication capabilities, and low training efficiency of distributed machine learning models, so as to solve the problem of low training efficiency and reduce impact Effect

Active Publication Date: 2022-05-10
GUANGDONG UNIV OF TECH
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Problems solved by technology

[0013] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a method that can solve the problem of low efficiency of distributed machine learning model training caused by the large difference in computing and communication capabilities of different terminal devices, and at the same time reduce the impact of data non-independent and identical distribution characteristics. Impact of Model Accuracy on Mobile Edge Distributed Machine Learning Systems

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  • A mobile edge distributed machine learning system and method
  • A mobile edge distributed machine learning system and method
  • A mobile edge distributed machine learning system and method

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with specific embodiment:

[0052] like Figure 1-3 As shown, a mobile edge distributed machine learning system includes an edge server and multiple terminal devices; wherein, the edge server includes a central decision-making module 1, a global model parameter aggregation module 2, and a server communication module 3; the server communication module 3 includes a response Information receiving sub-module 3-1, request information and decision information sending sub-module 3-2, local model parameter receiving sub-module 3-3 and global model parameter sending sub-module 3-4;

[0053] Each terminal device includes a central control module 4, a local model parameter update module 5, a data sample storage module 6, and a terminal communication module 7; the terminal communication module 7 includes a request information and decision information receiving sub-module 7-1, and a response information sending sub...

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Abstract

The invention discloses a mobile edge distributed machine learning system and method. In the method, D2D communication technology is used to unload data samples between terminal devices, and by adjusting the number of data samples stored by terminal devices participating in distributed machine learning, each terminal The data sample size of the device matches its computing power, which balances the time consumed by the computing and communication of each terminal device, and solves the problem of low efficiency of distributed machine learning model training caused by the large difference in computing and communication capabilities of different terminal devices. It can reduce the impact of data non-independent and identical distribution characteristics on model accuracy.

Description

technical field [0001] The present invention relates to the technical field of edge intelligence applications, in particular to a mobile edge distributed machine learning system and method. Background technique [0002] With the rapid development of the Internet of Things and artificial intelligence, network edge intelligence is an inevitable development trend. Distributed machine learning (distributed machine learning) is one of the important research directions, which effectively combines artificial intelligence (AI) and mobile edge computing (mobile edge computing, MEC) technology, at the edge of the network, through the joint edge server and Mass computing and terminal devices with limited communication capabilities realize distributed machine learning. Distributed machine learning can be divided into two training modes: synchronous and asynchronous. Since the asynchronous training mode has serious gradient failure problems, many research works are based on distributed ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50G06N20/00
CPCG06F9/5072G06N20/00
Inventor 许杰蔡晓然莫小鹏陈俊阳
Owner GUANGDONG UNIV OF TECH
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