Enabling High-Quality Machine Learning Model Trading on Blockchain-Based Marketplace

Chunxiao Li, Haodi Wang, Yu Zhao, Yuxin Xi, Enliang Xu and Shenling Wang ()
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Chunxiao Li: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Haodi Wang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Yu Zhao: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Yuxin Xi: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
Enliang Xu: School of Data Science and Artificial Intelligence, Dongbei University of Finance and Economics, Dalian 116025, China
Shenling Wang: School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China

Mathematics, 2023, vol. 11, issue 12, 1-25

Abstract: Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage its advantages on a broader scale. However, these markets face challenges in designing effective incentives for model owners to share their models, and for model users to provide honest feedback on model quality. This paper proposes a novel game theoretic framework for machine learning model sharing markets that addresses these challenges. Our framework includes two main components: a mechanism for incentivizing model owners to share their models, and a mechanism for encouraging the honest evaluation of model quality by the model users. To evaluate the effectiveness of our framework, we conducted experiments and the results demonstrate that our mechanism for incentivizing model owners is effective at encouraging high-quality model sharing, and our reputation system encourages the honest evaluation of model quality.

Keywords: model sharing; MLaaS; incentive mechanism; smart contract (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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