FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol

TL;DR

This paper introduces FEDSTR, a decentralized marketplace built on the NOSTR protocol that enables secure, censorship-resistant trading of datasets and AI model training services for federated learning and large language models.

Contribution

It proposes a novel marketplace architecture leveraging NOSTR for decentralized AI training, including a proof-of-concept implementation demonstrating feasibility.

Findings

Proof-of-concept implementation over public NOSTR relays

Decentralized marketplace enables fair AI training transactions

NOSTR's features support censorship-resistant AI service exchange

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, with 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. To demonstrate viability, we present a proof-of-concept implementation over public NOSTR relays. 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.