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

• URL: http://arxiv.org/abs/2112.02870v1
• Date: Mon, 6 Dec 2021 08:52:42 GMT
• Title: A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data
• Authors: Lam Duc Nguyen, Shashi Raj Pandey, Soret Beatriz, Arne Broering, and Petar Popovski
• Abstract summary: An emerging paradigm in Machine Learning (ML) is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. The problem of valuation of models, as well as the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the literature. In this paper, a new ecosystem of ML model trading over a trusted ML-based network is proposed. The buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality
• Score: 24.847898465750667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Machine Learning (ML) models are becoming increasingly complex, one of the central challenges is their deployment at scale, such that companies and organizations can create value through Artificial Intelligence (AI). An emerging paradigm in ML is a federated approach where the learning model is delivered to a group of heterogeneous agents partially, allowing agents to train the model locally with their own data. However, the problem of valuation of models, as well the questions of incentives for collaborative training and trading of data/models, have received limited treatment in the literature. In this paper, a new ecosystem of ML model trading over a trusted Blockchain-based network is proposed. The buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality. In doing so, the proportional relation between the local data and the quality of trained models is considered, and the valuations of seller's data in training the models are estimated through the distributed Data Shapley Value (DSV). At the same time, the trustworthiness of the entire trading process is provided by the distributed Ledger Technology (DLT). Extensive experimental evaluation of the proposed approach shows a competitive run-time performance, with a 15\% drop in the cost of execution, and fairness in terms of incentives for the participants.

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