Version 1
: Received: 22 April 2024 / Approved: 23 April 2024 / Online: 24 April 2024 (10:00:25 CEST)
How to cite:
Nikolakakis, K.; Chantzialexiou, G.; Kalogerias, D. FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol. Preprints2024, 2024041563. https://doi.org/10.20944/preprints202404.1563.v1
Nikolakakis, K.; Chantzialexiou, G.; Kalogerias, D. FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol. Preprints 2024, 2024041563. https://doi.org/10.20944/preprints202404.1563.v1
Nikolakakis, K.; Chantzialexiou, G.; Kalogerias, D. FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol. Preprints2024, 2024041563. https://doi.org/10.20944/preprints202404.1563.v1
APA Style
Nikolakakis, K., Chantzialexiou, G., & Kalogerias, D. (2024). FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol. Preprints. https://doi.org/10.20944/preprints202404.1563.v1
Chicago/Turabian Style
Nikolakakis, K., George Chantzialexiou and Dionysis Kalogerias. 2024 "FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol" Preprints. https://doi.org/10.20944/preprints202404.1563.v1
Abstract
The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR applications include but are not limited to direct messaging, file sharing, audio/video streaming, collaborative writing, blogging and data processing through distributed AI directories. In this work, we propose an approach that builds upon the existing protocol structure with end goal a decentralized marketplace for federated learning and LLM training. In this proposed design there are two parties: on one side there are customers who provide a dataset that they want to use for training an AI model. On the other side, there are service providers, who receive (parts of) the dataset, train the AI model, and for a payment as an exchange, they return the optimized AI model. The decentralized and censorship resistant features of the NOSTR enable the possibility of designing a fair and open marketplace for training AI models and LLMs.
Keywords
distributed; parallel; and cluster computing; social and information networks; federated learning; LLMs; communication protocols
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
