# Decentralized & Collaborative AI on Blockchain

arXiv: https://arxiv.org/abs/1907.07247 · HTML: https://mlsanity.com/paper/1907.07247/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 21 of 21)

- [Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities](https://mlsanity.com/paper/2111.11509/) (2021)
- [BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning](https://mlsanity.com/paper/2202.02817/) (2022)
- [DECORAIT -- DECentralized Opt-in/out Registry for AI Training](https://mlsanity.com/paper/2309.14400/) (2023)
- [Survey on the Convergence of Machine Learning and Blockchain](https://mlsanity.com/paper/2201.00976/) (2022)
- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [A Decentralized Approach towards Responsible AI in Social Ecosystems](https://mlsanity.com/paper/2102.06362/) (2021)
- [Blockchain-based Framework for Scalable and Incentivized Federated Learning](https://mlsanity.com/paper/2502.14170/) (2025)
- [DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities](https://mlsanity.com/paper/2105.08671/) (2021)
- [DeFL: Decentralized Weight Aggregation for Cross-silo Federated Learning](https://mlsanity.com/paper/2208.00848/) (2022)
- [The Design Principle of Blockchain: An Initiative for the SoK of SoKs](https://mlsanity.com/paper/2301.00479/) (2023)
- [A Review on Building Blocks of Decentralized Artificial Intelligence](https://mlsanity.com/paper/2402.02885/) (2024)
- [ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain](https://mlsanity.com/paper/2404.16967/) (2024)
- [Blockchain and Artificial Intelligence: Synergies and Conflicts](https://mlsanity.com/paper/2405.13462/) (2024)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [Long-term Data Sharing under Exclusivity Attacks](https://mlsanity.com/paper/2201.09137/) (2022)
- [An Investigation of Smart Contract for Collaborative Machine Learning Model Training](https://mlsanity.com/paper/2209.05017/) (2022)
- [Generation of Optimized Solidity Code for Machine Learning Models using LLMs](https://mlsanity.com/paper/2503.06203/) (2025)
- [Blockchain as an Enabler for Transfer Learning in Smart Environments](https://mlsanity.com/paper/2204.03959/) (2022)
- [Leveraging Centric Data Federated Learning Using Blockchain For Integrity Assurance](https://mlsanity.com/paper/2206.04731/) (2022)
- [APPFLChain: A Privacy Protection Distributed Artificial-Intelligence Architecture Based on Federated Learning and Consortium Blockchain](https://mlsanity.com/paper/2206.12790/) (2022)
- [PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace](https://mlsanity.com/paper/2307.15168/) (2023)

## Abstract (arXiv)

Machine learning has recently enabled large advances in artificial intelligence, but these tend to 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 re-train them. We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model. This model will be shared publicly on a blockchain where it can be free to use for inference. Ideal learning problems include scenarios where a model is used many times for similar input such as personal assistants, playing games, recommender systems, etc. In order to maintain the model's accuracy with respect to some test set we propose both financial and non-financial (gamified) incentive structures for providing good data. A free and open source implementation for the Ethereum blockchain is provided at https://github.com/microsoft/0xDeCA10B.