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Título del libro:
Título del capítulo: Fantastyc: Blockchain-Based Federated Learning Made Secure and Practical

Autores UNAM:
ALVARO GARCIA PEREZ;

Autores externos:

Idioma:

Año de publicación:
2024

Palabras clave:

Adversarial machine learning; Collaborative learning; Contrastive Learning; Decentralized systems; Transfer learning; Block-chain; Central servers; Decentralised; Distributed systems; Learning approach; Local data; Machine learning models; Multiple clients; State of the art; Federated learning

Resumen:

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. © 2024 IEEE.

Entidades citadas de la UNAM:

Título del libro: Título del capítulo: Fantastyc: Blockchain-Based Federated Learning Made Secure and Practical

Título del libro:
Título del capítulo: Fantastyc: Blockchain-Based Federated Learning Made Secure and Practical