Fantastyc: Blockchain-based Federated Learning Made Secure and Practical

• URL: http://arxiv.org/abs/2406.03608v2
• Date: Mon, 29 Jul 2024 14:54:22 GMT
• Title: Fantastyc: Blockchain-based Federated Learning Made Secure and Practical
• Authors: William Boitier, Antonella Del Pozzo, Álvaro García-Pérez, Stephane Gazut, Pierre Jobic, Alexis Lemaire, Erwan Mahe, Aurelien Mayoue, Maxence Perion, Tuanir Franca Rezende, Deepika Singh, Sara Tucci-Piergiovanni,
• Abstract summary: Federated Learning is a decentralized framework that enables 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. We propose Fantastyc, a solution designed to address these challenges that have been never met together in the state of the art.
• Score: 0.7083294473439816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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