Federal learning method and system for e-commerce platform
The invention relates to a federal learning method and system for an e-commerce platform, and relates to the technical field of data security and privacy protection, and the method comprises the steps: generating a non-independent identically distributed data model according to the type of a client,...
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| creator | LIANG HUANGHUANG CHENG DAZHAO HU CHUANG XU FENG YAN WEILIN |
| description | The invention relates to a federal learning method and system for an e-commerce platform, and relates to the technical field of data security and privacy protection, and the method comprises the steps: generating a non-independent identically distributed data model according to the type of a client, and obtaining a skewness value of the client; selecting a plurality of clients from the client pool as training data providers according to the meta-parameters; and evaluating an output result of the federal learning model, and feeding back and adjusting meta parameters until a model evaluation result reaches an expectation. According to the method, the data isomerism of the client is deduced and privacy protection is performed by constructing the non-independent identically distributed data model aiming at the data characteristics of the e-commerce platform, and the quality of the training data is improved by introducing the meta-parameter balance federated learning model to train the required data volume and the |
| format | Patent |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING PHYSICS |
| title | Federal learning method and system for e-commerce platform |
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