An Efficient and Privacy-Preserving Federated Learning Approach Based on Homomorphic Encryption

Federated Learning (FL) is a decentralized and collaborative learning approach that ensures the data privacy of each participant. However, recent studies have shown that the private data of each participant can be obtained from shared parameters of local models through reversal model and membership...

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Библиографические подробности

Главные авторы:	Francesco Castro, Donato Impedovo, Giuseppe Pirlo
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2025-01-01
Серии:	IEEE Open Journal of the Computer Society
Предметы:	BFV distributed data federated learning homomorphic encryption privacy-preserving
Online-ссылка:	https://ieeexplore.ieee.org/document/10858339/
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