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As Machine Learning (ML) models are becoming increasingly complex, one of thecentral challenges is their deployment at scale, such that companies andorganizations can create value through Artificial Intelligence (AI). Anemerging paradigm in ML is a federated approach where the learning model isdelivered to a group of heterogeneous agents partially, allowing agents totrain the model locally with their own data. However, the problem of valuationof models, as well the questions of incentives for collaborative training andtrading of data/models, have received limited treatment in the literature. Inthis paper, a new ecosystem of ML model trading over a trusted Blockchain-basednetwork is proposed. The buyer can acquire the model of interest from the MLmarket, and interested sellers spend local computations on their data toenhance that model's quality. In doing so, the proportional relation betweenthe local data and the quality of trained models is considered, and thevaluations of seller's data in training the models are estimated through thedistributed Data Shapley Value (DSV). At the same time, the trustworthiness ofthe entire trading process is provided by the distributed Ledger Technology(DLT). Extensive experimental evaluation of the proposed approach shows acompetitive run-time performance, with a 15\% drop in the cost of execution,and fairness in terms of incentives for the participants.

A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data