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Block chain assisted federal learning active content caching method in fog computing

A content caching, blockchain technology, applied in neural learning methods, ensemble learning, biological neural network models, etc., can solve problems such as poor communication security, affecting training results, inserting malicious data, etc., to reduce training time, improve The training process and the effect of improving prediction accuracy

Pending Publication Date: 2021-12-31
CHONGQING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In addition, due to poor communication security in the current IoT architecture, there are data reliability issues, such as data loss, insertion of malicious data, etc.
This phenomenon will also cause inaccurate update parameters of the uploaded model, which will further affect the training results.

Method used

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  • Block chain assisted federal learning active content caching method in fog computing
  • Block chain assisted federal learning active content caching method in fog computing
  • Block chain assisted federal learning active content caching method in fog computing

Examples

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Embodiment Construction

[0033]Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematically illustrating the basic idea of ​​the present invention, and the following embodiments and the features in the embodiments can be combined with each other under the condition of no conflict.

[0034] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should not be constr...

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Abstract

The invention relates to a block chain assisted federal learning active content caching method in fog computing, and belongs to the technical field of mobile communication. Firstly, in consideration of resource constraints of user equipment in an actual network, a federated learning scheme based on user selection is provided, so that the training process of federated learning is effectively improved, and the model training time is shortened. Secondly, a context-aware antagonistic automatic encoder (C-AAE) is used to predict the highly dynamic content popularity. And finally, in order to ensure the security of model update parameter uploading in federated learning, a block chain technology is introduced, and four decentralized entity-oriented smart contracts for recording and verifying transactions are designed. The cache hit rate can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a federated learning active content caching method assisted by blockchain in fog computing. Background technique [0002] Content caching technology is considered to be a promising solution. It can reduce backhaul link congestion and user request content delay by storing highly popular content in edge devices in advance, and improve user satisfaction. Due to the limited storage resources of edge nodes, the content that is most likely to be requested by users must be placed in the local cache. Traditional cache mechanisms such as First-In-First-Out (FIFO), Least Recently Used (LRU) and Least Frequently Used (LFU) update cache content through static rules. However, they are not suitable for predicting dynamically changing popular content. [0003] Currently, many studies utilize machine learning methods to learn content popularity by observing users' historical content...

Claims

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Application Information

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IPC IPC(8): H04W16/22H04W28/14G06N3/04G06N3/08G06N20/20
CPCH04W16/22H04W28/14G06N20/20G06N3/08G06N3/045
Inventor 崔太平彭贻林展骞谢志杰周梓翔雷一达文梓怡
Owner CHONGQING UNIV OF POSTS & TELECOMM
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