Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain
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Abstract (arXiv)
Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2018.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
- 2022Survey on the Convergence of Machine Learning and Blockchaincited by 4
- 2024A Review on Building Blocks of Decentralized Artificial Intelligencecited by 2
- 2022An Investigation of Smart Contract for Collaborative Machine Learning Model Trainingcited by 1
- 2025Generation of Optimized Solidity Code for Machine Learning Models using LLMscited by 1
- 2022Blockchain as an Enabler for Transfer Learning in Smart Environmentscited by 0
- 2022Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurancecited by 0
- 2026I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoningcited by 0
- 2026I Know What I Don't Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning13 Mar 2026
- 2025Generation of Optimized Solidity Code for Machine Learning Models using LLMs8 Mar 2025
- 2024A Review on Building Blocks of Decentralized Artificial Intelligence5 Feb 2024
- 2022An Investigation of Smart Contract for Collaborative Machine Learning Model Training12 Sep 2022
- 2022Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance9 Jun 2022
- 2022Blockchain as an Enabler for Transfer Learning in Smart Environments8 Apr 2022
- 2022Survey on the Convergence of Machine Learning and Blockchain4 Jan 2022
- 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System11 Nov 2021