  Decentralized Intelligence Network (DIN) is a theoretical framework
addressing data fragmentation and siloing challenges, enabling scalable AI
through data sovereignty. It facilitates effective AI utilization within
sovereign 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.
