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

arXiv: https://arxiv.org/abs/2404.15834 · HTML: https://mlsanity.com/paper/2404.15834/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 15 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills](https://mlsanity.com/paper/2308.16369/) (2023)
- [Accelerating LLM Inference with Staged Speculative Decoding](https://mlsanity.com/paper/2308.04623/) (2023)
- [DiLoCo: Distributed Low-Communication Training of Language Models](https://mlsanity.com/paper/2311.08105/) (2023)
- [SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference](https://mlsanity.com/paper/2307.02628/) (2023)
- [FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models](https://mlsanity.com/paper/2310.10049/) (2023)
- [Faster On-Device Training Using New Federated Momentum Algorithm](https://mlsanity.com/paper/2002.02090/) (2020)
- [Efficient LLM Inference on CPUs](https://mlsanity.com/paper/2311.00502/) (2023)
- [FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs](https://mlsanity.com/paper/2309.01172/) (2023)
- [Bluesky and the AT Protocol: Usable Decentralized Social Media](https://mlsanity.com/paper/2402.03239/) (2024)
- [BOINC: A Platform for Volunteer Computing](https://mlsanity.com/paper/1903.01699/) (2019)
- [Everything is a Race and Nakamoto Always Wins](https://mlsanity.com/paper/2005.10484/) (2020)
- [Exploring the Robustness of Decentralized Training for Large Language Models](https://mlsanity.com/paper/2312.00843/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Survey: Towards Privacy and Security in Mobile Large Language Models](https://mlsanity.com/paper/2509.02411/) (2025)
- [A Lifecycle Cost Analysis of Smart-Contract-Coordinated Federated Learning Marketplaces](https://mlsanity.com/paper/2609.13170/) (2026)

## Abstract (arXiv)

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.