Federal attention DBN collaborative detection system based on client selection
A collaborative detection and attention technology, applied in the field of data processing, can solve the problems of power consumption information data sensitivity, low learning efficiency, and vulnerability to network attacks
Pending Publication Date: 2021-09-14
CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology
Since AMI is a key system of the smart grid, it is vulnerable to network attacks, and the data collected by the smart meter is sensitive, which may cause privacy leakage. The detection model of related technologies usually has "low learning efficiency", " Model training accuracy is not high" and "privacy leak" and other technical problems
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[0045] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0046]It should be noted that although the functional modules are divided in the system schematic diagram and the logical order is shown in the flow chart, in some cases, it can be executed in a different order than the module division in the system or the flow chart steps shown or described. The terms "first", "second" and the like in the specification and claims and the above drawings are used to distinguish similar objects, and not necessarily used to describe a specific sequence or sequence.
[0047] The security of the smart grid is becoming more and more important. The advanced measurement s...
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The invention discloses a federal attention DBN collaborative detection system based on client selection, and the system comprises: a plurality of intelligent electric meters which are used for collecting power data; a plurality of concentrators, each concentrator being in communication connection with a plurality of presidial intelligent electric meters and used for acquiring power data from the corresponding intelligent electric meters and training the power data by using a DBN training model with an attention mechanism to obtain training parameters; and a data center which is in communication connection with all the concentrators, and is used for acquiring the training parameters from each concentrator, performing federated average aggregation on the training parameters, allocating a DBN training model to the concentrators according to resources of the concentrators, and sending a federated average aggregation result to the next round of concentrator, so that the concentrators train the DBN training model to be converged. The efficiency of federal learning can be effectively improved, the model training precision is improved, and the security of data privacy is improved.
Description
technical field [0001] The invention relates to the technical field of data processing, in particular to a federated attention DBN collaborative detection system based on client selection. Background technique [0002] The information security of the smart grid is becoming more and more important. The advanced measurement system AMI is an important part of the smart grid. The security of the advanced metering system is an urgent problem that needs to be solved urgently for the security of the smart grid. Since AMI is a key system of the smart grid, it is vulnerable to network attacks, and the data collected by the smart meter is sensitive, which may cause privacy leakage. The detection model of related technologies usually has "low learning efficiency", " Model training accuracy is not high" and "privacy leakage" and other technical problems. Contents of the invention [0003] The present invention aims to solve at least the technical problems existing in the prior art. ...
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Login to View More IPC IPC(8): G06K9/62G06F21/62G06N3/08
CPCG06F21/6245G06N3/084G06F18/214Y04S10/50
Inventor 夏卓群陈亚玲尹波文琴
Owner CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY