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June 5, 2024· arXiv
conference-paper
Open access

Fantastyc: Blockchain-based Federated Learning Made Secure and Practical

Authors:William BoitierAntonella Del PozzoÁlvaro García-PérezStéphane GazutPierre JobicAlexis LemaireErwan MaheAurélien MayoueMaxence PerionTuanir França RezendeDeepika SinghSara Tucci-Piergiovanni

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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