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.

通过基于 w3c websockets 标准的 NOSTR 通信协议，我们提出了一个在现有协议结构上构建的去中心化市场，用于联邦学习和 LLM 训练，提供了一个公平和开放的 AI 模型和 LLM 训练市场。

FEDSTR：AI 转型之路 - 基于 NOSTR 协议的联合学习与 LLM 培训的去中心化市场

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

The social Web is transforming the way information is created and
distributed. Blog authoring tools enable users to publish content, while sites
such as Digg and Del.icio.us are used to distribute content to a wider
audience. With content fast becoming a commodity, interest in using social
networks to promote and find content has grown, both on the side of content
producers (viral marketing) and consumers (recommendation). Here we study the
role of social networks in promoting content on Digg, a social news aggregator
that allows users to submit links to and vote on news stories. Digg's goal is
to feature the most interesting stories on its front page, and it aggregates
opinions of its many users to identify them. Like other social networking
sites, Digg allows users to designate other users as ``friends'' and see what
stories they found interesting. We studied the spread of interest in news
stories submitted to Digg in June 2006. Our results suggest that pattern of the
spread of interest in a story on the network is indicative of how popular the
story will become. Stories that spread mainly outside of the submitter's
neighborhood go on to be very popular, while stories that spread mainly through
submitter's social neighborhood prove not to be very popular. This effect is
visible already in the early stages of voting, and one can make a prediction
about the potential audience of a story simply by analyzing where the initial
votes come from.

本研究探讨了社交网络在 Digg 上推广内容方面的作用，并发现了在 Digg 上的新闻散布方式会影响新闻的流行程度。