# A Review on Building Blocks of Decentralized Artificial Intelligence

arXiv: https://arxiv.org/abs/2402.02885 · HTML: https://mlsanity.com/paper/2402.02885/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 113 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2105.07447/) (2021)
- [Decentralized & Collaborative AI on Blockchain](https://mlsanity.com/paper/1907.07247/) (2019)
- [PIRATE: A Blockchain-based Secure Framework of Distributed Machine Learning in 5G Networks](https://mlsanity.com/paper/1912.07860/) (2019)
- [BitTensor: A Peer-to-Peer Intelligence Market](https://mlsanity.com/paper/2003.03917/) (2020)
- [OmniLytics: A Blockchain-based Secure Data Market for Decentralized Machine Learning](https://mlsanity.com/paper/2107.05252/) (2021)
- [Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain](https://mlsanity.com/paper/1802.10185/) (2018)
- [A Marketplace for Trading AI Models based on Blockchain and Incentives for IoT Data](https://mlsanity.com/paper/2112.02870/) (2021)
- [ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks](https://mlsanity.com/paper/1802.01746/) (2018)
- [A Hitchhiker's Guide On Distributed Training of Deep Neural Networks](https://mlsanity.com/paper/1810.11787/) (2018)
- [Marketplace for AI Models](https://mlsanity.com/paper/2003.01593/) (2020)
- [Ownership preserving AI Market Places using Blockchain](https://mlsanity.com/paper/2001.09011/) (2020)
- [IPFS and Friends: A Qualitative Comparison of Next Generation Peer-to-Peer Data Networks](https://mlsanity.com/paper/2102.12737/) (2021)
- [Multi-agent systems and decentralized artificial superintelligence](https://mlsanity.com/paper/1702.08529/) (2017)
- [A Unified Analytical Framework for Trustable Machine Learning and Automation Running with Blockchain](https://mlsanity.com/paper/1903.08801/) (2019)
- [Should Artificial Intelligence Governance be Centralised? Design Lessons from History](https://mlsanity.com/paper/2001.03573/) (2020)
- [DaiMoN: A Decentralized Artificial Intelligence Model Network](https://mlsanity.com/paper/1907.08377/) (2019)
- [Blockchain Enabled Trustless API Marketplace](https://mlsanity.com/paper/1812.02154/) (2018)
- [Libraries, Integrations and Hubs for Decentralized AI using IPFS](https://mlsanity.com/paper/2210.16651/) (2022)
- [Is Decentralized AI Safer?](https://mlsanity.com/paper/2211.05828/) (2022)

## Built on by (most cited first, 2 of 2)

- [Is Decentralized AI Governable? From Regulative Policy to Constitutive Protocol](https://mlsanity.com/paper/2605.24538/) (2026)
- [Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions](https://mlsanity.com/paper/2607.24589/) (2026)

## Abstract (arXiv)

Artificial intelligence is transforming our lives, and technological progress and transfer from the academic and theoretical sphere to the real world are accelerating yearly. But during that progress and transition, several open problems and questions need to be addressed for the field to develop ethically, such as digital privacy, ownership, and control. These are some of the reasons why the currently most popular approaches of artificial intelligence, i.e., centralized AI (CEAI), are questionable, with other directions also being widely explored, such as decentralized artificial intelligence (DEAI), to solve some of the most reaching problems. This paper provides a systematic literature review (SLR) of existing work in the field of DEAI, presenting the findings of 71 identified studies. The paper's primary focus is identifying the building blocks of DEAI solutions and networks, tackling the DEAI analysis from a bottom-up approach. In the end, future directions of research and open problems are proposed.