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.

翻译：NOSTR是一种基于w3c WebSocket标准的社交网络通信协议。尽管仍处于发展初期，它已作为社交媒体协议广为人知，拥有数千可信用户及多种用户界面，提供独特的体验与强大功能。其应用场景包括但不限于：直接消息传递、文件共享、音视频流传输、协同写作、博客发布以及通过分布式AI目录进行数据处理。本研究提出一种基于现有协议架构的方法，旨在构建用于联邦学习与LLM训练的去中心化市场。该设计包含两方参与者：一方是提供训练数据集的需求方客户，另一方是接收（部分）数据集、训练AI模型并通过交付优化后模型获取报酬的服务提供方。为验证可行性，我们在公共NOSTR中继网络上实现了概念验证系统。NOSTR协议的去中心化与抗审查特性，为构建公平开放的AI模型及LLM训练市场提供了可能。