Fantastyc: Blockchain-based Federated Learning Made Secure and Practical

Autor:	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
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.03608 Zobrazit plný text záznamu View this record from Arxiv