Document detail
ID
oai:arXiv.org:2406.03608
Topic
Computer Science - Cryptography an... Computer Science - Distributed, Pa...Author
Boitier, William Del Pozzo, Antonella García-Pérez, Álvaro Gazut, Stephane Jobic, Pierre Lemaire, Alexis Mahe, Erwan Mayoue, Aurelien Perion, Maxence Rezende, Tuanir Franca Singh, Deepika Tucci-Piergiovanni, SaraCategory
Computer Science
Year
2024
listing date
7/31/2024
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.
Boitier, William,Del Pozzo, Antonella,García-Pérez, Álvaro,Gazut, Stephane,Jobic, Pierre,Lemaire, Alexis,Mahe, Erwan,Mayoue, Aurelien,Perion, Maxence,Rezende, Tuanir Franca,Singh, Deepika,Tucci-Piergiovanni, Sara, 2024, Fantastyc: Blockchain-based Federated Learning Made Secure and Practical
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