Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning

Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning

Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling. Since the labels on records may contain sensitive information, privacy-preserving mechanisms...

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Bibliographic Details
Main Authors:	Hendra Kurniawan, Masahiro Mambo
Format:	Article
Language:	English
Published:	MDPI AG 2022-10-01
Series:	Entropy
Subjects:	privacy preserving federated learning active learning homomorphic encryption
Online Access:	https://www.mdpi.com/1099-4300/24/11/1545

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