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.
@article{arxiv.2406.03608,
title = {Fantastyc: Blockchain-based Federated Learning Made Secure and Practical},
author = {William Boitier and Antonella Del Pozzo and Álvaro García-Pérez and Stephane Gazut and Pierre Jobic and Alexis Lemaire and Erwan Mahe and Aurelien Mayoue and Maxence Perion and Tuanir Franca Rezende and Deepika Singh and Sara Tucci-Piergiovanni},
journal= {arXiv preprint arXiv:2406.03608},
year = {2024}
}