Decentralized Intelligence Network (DIN) ...

• Main Author: Nash, Abraham
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2024
• Subjects: Cryptography and Security cs.CR; Computers and Society cs.CY; Distributed, Parallel, and Cluster Computing cs.DC; Emerging Technologies cs.ET; Machine Learning cs.LG; FOS: Computer and information sciences; Roosevelt Island
• Online Access: https://dx.doi.org/10.48550/arxiv.2407.02461; https://arxiv.org/abs/2407.02461

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 ... : 14 pages, 1 figure. DIN was presented by the author as a speaker at the Summit on Responsible Decentralized Intelligence - Future of Decentralization and AI, hosted by Berkeley RDI on August 6, 2024, at the Verizon Center, Cornell Tech Campus, Roosevelt Island, NYC ...