Fantastyc: Blockchain-Based Federated Learning Made Secure and Practical
William Boitier, Antonella Del Pozzo, Álvaro García-Pérez, Stéphane Gazut, Pierre Jobic, Alexis Lemaire, Erwan Mahe, Aurélien Mayoue, Maxence Perion, Tuanir França Rezende, Deepika Singh, Sara Tucci-Piergiovanni · 2024
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.