FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol
Konstantinos E. Nikolakakis, George Chantzialexiou, Dionysis Kalogerias

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)…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data
Methodstravel james
