This paper proposes an evaluation mechanism of supply chain credit that integrates federated learning and block chain technology. In order to ensure the effective use of energy data, a set of credit evaluation index is designed for supply chain enterprises data and their energy data. Meanwhile, under the condition that data privacy is guaranteed, a federated learning algorithm is designed to improve the accuracy of federated learning training with the data of supply chain enterprises. Block chain and federated learning technology are integrated to assess credit. On the basis of ensuring data security, the credit evaluation results stored on the alliance chain are used for a second time, which not only ensures the interests of data requesters, but also ensures the risk control of external enterprises participating in supply chain finance.


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    Titel :

    Research on trusted sharing and privacy computing technology of energy data in supply chain based on alliance chain


    Beteiligte:
    Wang, Xinyan (Autor:in) / Zhu, Ying (Autor:in) / Yu, Xiaokun (Autor:in) / Liu, Yong (Autor:in) / Hu, An (Autor:in) / Jia, Jingli (Autor:in) / Wang, Zhiyong (Autor:in)


    Erscheinungsdatum :

    2022-10-12


    Format / Umfang :

    1184271 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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