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Survey on the Convergence of Machine Learning and Blockchain

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Intelligent Systems and Applications (IntelliSys 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 543))

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Abstract

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without an update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exist. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we survey the convergence of collaborative ML and blockchain. Different ways of the combination of these two technologies are investigated and their fields of application are examined. Discussion on the limitations of current research and their future directions are also included.

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Notes

  1. 1.

    DanKu Contract is named after the combination of the family names of its developers, K. Daniel and A. B. Kurtulmus.

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Author information

Authors and Affiliations

  1. Shanghai, China

    Shengwen Ding

  2. Cambridge, MA, USA

    Chenhui Hu

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  1. Shengwen Ding
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  2. Chenhui Hu
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Corresponding author

Correspondence to Shengwen Ding .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Ding, S., Hu, C. (2023). Survey on the Convergence of Machine Learning and Blockchain. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 543. Springer, Cham. https://doi.org/10.1007/978-3-031-16078-3_10

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