VPPFL: Verifiable Privacy-Preserving Federated Learning in Cloud Environment

As a distributed machine learning paradigm, federated learning has attracted wide attention from academia and industry by enabling multiple users to jointly train models without sharing local data. However, federated learning still faces various security and privacy issues. First, even if users only...

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Detalles Bibliográficos

Autores principales:	Huiyong Wang, Tengfei Yang, Yong Ding, Shijie Tang, Yujue Wang
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Privacy protection federated learning verifiable threshold multi-key homomorphic encryption
Acceso en línea:	https://ieeexplore.ieee.org/document/10703046/
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