# Analysis of Models for Decentralized and Collaborative AI on Blockchain

arXiv: https://arxiv.org/abs/2009.06756 · HTML: https://mlsanity.com/paper/2009.06756/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [Blockchain-based Framework for Scalable and Incentivized Federated Learning](https://mlsanity.com/paper/2502.14170/) (2025)
- [ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain](https://mlsanity.com/paper/2404.16967/) (2024)
- [Generation of Optimized Solidity Code for Machine Learning Models using LLMs](https://mlsanity.com/paper/2503.06203/) (2025)
- [Heart Diseases Prediction Using Block-chain and Machine Learning](https://mlsanity.com/paper/2306.01817/) (2023)
- [PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace](https://mlsanity.com/paper/2307.15168/) (2023)
- [SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence](https://mlsanity.com/paper/2605.10515/) (2026)

## Abstract (arXiv)

Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Na"ive Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards. A free and open source implementation for the Ethereum blockchain and simulations written in Python is provided at https://github.com/microsoft/0xDeCA10B. This version has updated gas costs using newer optimizations written after the original publication.