# Fantastyc: Blockchain-based Federated Learning Made Secure and Practical

arXiv: https://arxiv.org/abs/2406.03608 · HTML: https://mlsanity.com/paper/2406.03608/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2024.

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

- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [zkFL-Health: Blockchain-Enabled Zero-Knowledge Federated Learning for Medical AI Privacy](https://mlsanity.com/paper/2512.21048/) (2025)

## Abstract (arXiv)

Federated Learning is a decentralized framework that enables multiple clients to collaboratively train a machine learning model under the orchestration of a central server without sharing their local data. The centrality of this framework represents a point of failure which is addressed in literature by blockchain-based federated learning approaches. While ensuring a fully-decentralized solution with traceability, such approaches still face several challenges about integrity, confidentiality and scalability to be practically deployed. In this paper, we propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.