VPPFL: Verifiable Privacy-Preserving Federated Learning in Cloud Environment

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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Huiyong Wang, Tengfei Yang, Yong Ding, Shijie Tang, Yujue Wang
Format: Artykuł
Język:English
Wydane: IEEE 2024-01-01
Seria:IEEE Access
Hasła przedmiotowe:
Privacy protection
federated learning
verifiable
threshold multi-key homomorphic encryption
Dostęp online:https://ieeexplore.ieee.org/document/10703046/

Internet

https://ieeexplore.ieee.org/document/10703046/

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