Multi-terminal model compression method and device based on knowledge federation, task prediction method and device and electronic equipment

A compression method and model technology, applied in the field of artificial intelligence, can solve problems such as frequent communication and encrypted data exchange, high communication pressure, and reduced model training efficiency

Pending Publication Date: 2020-12-08
TONGDUN HLDG CO LTD
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AI Technical Summary

Problems solved by technology

[0005] However, when training the model, due to the participation of multiple parties in the training, frequent communication and encrypted data exchange are required, which brings a huge challenge to the communication volume
Moreover, as the amount of data and the number of participants incre

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  • Multi-terminal model compression method and device based on knowledge federation, task prediction method and device and electronic equipment
  • Multi-terminal model compression method and device based on knowledge federation, task prediction method and device and electronic equipment
  • Multi-terminal model compression method and device based on knowledge federation, task prediction method and device and electronic equipment

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Embodiment Construction

[0027] The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0028] Neural network (NN) is a complex network system formed by a large number of simple processing units (or called neurons) widely interconnected, which reflects many basic features of human brain function. The functions and characteristics of neurons can be imitated through mathematical models, so that a network model (also referred to as a network model of neural network in this application) can be constructed based on the mathematical model of neurons.

[0029] As in the background technology, although k...

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Abstract

The invention discloses a multi-terminal model compression method and device based on knowledge federation, a task prediction method and device and electronic equipment. The multi-terminal model compression method comprises the following steps: aggregating N-th trained local models reported by a plurality of participants to obtain a global to-be-compressed model, wherein N is greater than or equalto 1; compressing the global to-be-compressed model based on a preset performance index by adopting a common data set to obtain a global compression model, wherein the preset performance index is used for representing a performance index of the global compression model during prediction, and the common data set is obtained by performing data enhancement on the data of the plurality of participants; and sending the global compression model to the plurality of participants to carry out N + 1 rounds of training.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence, in particular to a multi-terminal model compression method, task prediction method, device and electronic equipment. Background technique [0002] As artificial intelligence (AI) matures, people have seen the great potential of artificial intelligence (AI) in complex application scenarios. Examples include self-driving cars, healthcare, financial data analysis, and more. People hope to explore the deeper advantages of artificial intelligence and improve the robustness and accuracy of the model. The current interest in artificial intelligence is driven by big data: In 2016, AlphaGo used a total of 300,000 chess positions as training data to achieve excellent results. [0003] With the success of AlphaGo, people naturally hope that big data-driven AI like AlphaGo can be realized in all aspects of our lives as soon as possible. However, the real-world picture is somewhat disappoin...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/082G06N3/084G06N3/045
Inventor 韦达孟丹李宏宇李晓林
Owner TONGDUN HLDG CO LTD
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