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Large-scale edge machine learning training method based on probabilistic sampling

A technology of learning training and machine learning, applied in machine learning, instruments, computing models, etc., can solve problems such as unguaranteed algorithm convergence and reduced model accuracy, and achieve the goals of increasing scalability, ensuring accuracy, and improving accuracy Effect

Active Publication Date: 2021-06-18
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

However, while this method maximizes the system process, completely out-of-sync nodes also introduce a large number of errors, so the convergence of the algorithm is often not guaranteed, which directly leads to the reduction of model accuracy.

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  • Large-scale edge machine learning training method based on probabilistic sampling
  • Large-scale edge machine learning training method based on probabilistic sampling
  • Large-scale edge machine learning training method based on probabilistic sampling

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Embodiment Construction

[0036] In order to make the object, technical scheme and advantages of the present invention clearer, the following in conjunction with the attached Figure 1-3 The specific embodiment of the learning and training method of the present invention is further described in detail.

[0037] Through probabilistic node selection, the progress of each node in distributed computing is only determined by a certain subset of the overall nodes, thereby reducing communication overhead and improving the overall progress of the system while ensuring that the calculation accuracy is only limited. For this trade-off, in order to quantitatively investigate the impact of this method on system performance, it is necessary to analyze the convergence of the probabilistic node selection method based on mathematical modeling. The method of the present invention is based on the convergence analysis of the existing synchronization method , the inconsistency (inconsistency) R[X] in the distributed syste...

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Abstract

The invention relates to a large-scale edge machine learning training method based on probabilistic sampling. The training method mainly comprises the following steps: 1, setting a training model and training parameters in a server; 2, performing data preprocessing on each device and preparing a local training data set; and 3, uploading gradient parameters obtained by local training to each device, uploading the gradient parameters to a server and the like. The learning training method has the advantages that large-scale edge equipment can be effectively trained, through probabilistic sampling, the process of judging synchronization is not limited by the size of the scale any more, the expandability of a training system can be effectively improved, large-scale edge training is supported, conciseness and effectiveness of the learning training method are shown.

Description

technical field [0001] The invention relates to the technical field of large-scale edge machine learning, in particular to a probabilistic sampling-based large-scale edge machine learning training method. Background technique [0002] With the popularization of edge computing devices, edge machine learning technology supports data acquisition and analysis requirements in a large number of intelligent applications. It has high practical application value in intelligent services and other issues. For example, a large number of high-definition cameras are distributed in a smart city traffic system to obtain real-time data. These massive video data need to be analyzed and processed in a timely manner, used to update the intelligent model deployed at the edge, and applied to different traffic scenarios. Another example is that intelligent services such as input methods and voice services in widely popular mobile phones need to generate personalized services based on the analysis...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/00G06K9/62
CPCG06N20/00G06F18/214
Inventor 赵健鑫韩锐刘驰
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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