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

arXiv: https://arxiv.org/abs/2112.02870 · HTML: https://mlsanity.com/paper/2112.02870/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 46 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [A Principled Approach to Data Valuation for Federated Learning](https://mlsanity.com/paper/2009.06192/) (2020)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [A Marketplace for Data: An Algorithmic Solution](https://mlsanity.com/paper/1805.08125/) (2018)
- [On the Outsized Importance of Learning Rates in Local Update Methods](https://mlsanity.com/paper/2007.00878/) (2020)
- [Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset](https://mlsanity.com/paper/2010.08006/) (2020)
- [A Crowdsourcing Framework for On-Device Federated Learning](https://mlsanity.com/paper/1911.01046/) (2019)
- [Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms](https://mlsanity.com/paper/2007.12004/) (2020)
- [Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR](https://mlsanity.com/paper/1811.08531/) (2018)
- [Trusted Wireless Monitoring based on Blockchain over NB-IoT Connectivity](https://mlsanity.com/paper/2004.06299/) (2020)
- [Ethna: Analyzing the Underlying Peer-to-Peer Network of the Ethereum Blockchain](https://mlsanity.com/paper/2010.01373/) (2020)
- [Achieving Data Truthfulness and Privacy Preservation in Data Markets](https://mlsanity.com/paper/1812.03280/) (2018)
- [Sensing as a Service (S2aaS): Buying and Selling IoT Data](https://mlsanity.com/paper/1702.02380/) (2017)
- [Mind My Value: a decentralized infrastructure for fair and trusted IoT data trading](https://mlsanity.com/paper/1807.02846/) (2018)
- [A Decentralized IoT Data Marketplace](https://mlsanity.com/paper/1906.01799/) (2019)

## Built on by (most cited first, 8 of 8)

- [FedToken: Tokenized Incentives for Data Contribution in Federated Learning](https://mlsanity.com/paper/2209.09775/) (2022)
- [A Contribution-based Device Selection Scheme in Federated Learning](https://mlsanity.com/paper/2203.05369/) (2022)
- [Strategic Coalition for Data Pricing in IoT Data Markets](https://mlsanity.com/paper/2206.07785/) (2022)
- [Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2303.06546/) (2023)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [AI-Based Crypto Tokens: The Illusion of Decentralized AI?](https://mlsanity.com/paper/2505.07828/) (2025)
- [Privacy-Aware Data Acquisition under Data Similarity in Regression Markets](https://mlsanity.com/paper/2312.02611/) (2023)
- [Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game](https://mlsanity.com/paper/2405.15154/) (2024)

## Abstract (arXiv)

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.