# Decentralized Intelligence Network (DIN)

arXiv: https://arxiv.org/abs/2407.02461 · HTML: https://mlsanity.com/paper/2407.02461/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 71 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 18 of 18)

- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation](https://mlsanity.com/paper/1802.07228/) (2018)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [BAFFLE : Blockchain Based Aggregator Free Federated Learning](https://mlsanity.com/paper/1909.07452/) (2019)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [IPLS : A Framework for Decentralized Federated Learning](https://mlsanity.com/paper/2101.01901/) (2021)
- [BlockFLow: An Accountable and Privacy-Preserving Solution for Federated Learning](https://mlsanity.com/paper/2007.03856/) (2020)
- [Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures](https://mlsanity.com/paper/1610.07997/) (2016)
- [A blockchain-orchestrated Federated Learning architecture for healthcare consortia](https://mlsanity.com/paper/1910.12603/) (2019)
- [FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data](https://mlsanity.com/paper/1807.03227/) (2018)
- [2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments](https://mlsanity.com/paper/2011.07516/) (2020)
- [Libertas: Privacy-Preserving Collective Computation for Decentralised Personal Data Stores](https://mlsanity.com/paper/2309.16365/) (2023)
- [Differential Privacy in Privacy-Preserving Big Data and Learning: Challenge and Opportunity](https://mlsanity.com/paper/2112.01704/) (2021)
- [Secret Sharing Sharing For Highly Scalable Secure Aggregation](https://mlsanity.com/paper/2201.00864/) (2022)

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

- [Decentralized Health Intelligence Network (DHIN)](https://mlsanity.com/paper/2408.06240/) (2024)
- [Intelligent System of Emergent Knowledge: A Coordination Fabric for Billions of Minds](https://mlsanity.com/paper/2506.09335/) (2025)

## Abstract (arXiv)

Decentralized Intelligence Network (DIN) is a theoretical framework designed to address challenges in AI development, particularly focusing on data fragmentation and siloing issues. It facilitates effective AI training within sovereign data networks by overcoming barriers to accessing diverse data sources, leveraging: 1) personal data stores to ensure data sovereignty, where data remains securely within Participants' control; 2) a scalable federated learning protocol implemented on a public blockchain for decentralized AI training, where only model parameter updates are shared, keeping data within the personal data stores; and 3) a scalable, trustless cryptographic rewards mechanism on a public blockchain to incentivize participation and ensure fair reward distribution through a decentralized auditing protocol. This approach guarantees that no entity can prevent or control access to training data or influence financial benefits, as coordination and reward distribution are managed on the public blockchain with an immutable record. The framework supports effective AI training by allowing Participants to maintain control over their data, benefit financially, and contribute to a decentralized, scalable ecosystem that leverages collective AI to develop beneficial algorithms.