Privacy-preserving federated learning based on partial low-quality data

Traditional machine learning requires collecting data from participants for training, which may lead to malicious acquisition of privacy in participants’ data. Federated learning provides a method to protect participants’ data privacy by transferring the training process from a centralized server to...

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Vydané v:	Journal of cloud computing : advances, systems and applications Ročník 13; číslo 1; s. 62 - 16
Hlavní autori:	Wang, Huiyong, Wang, Qi, Ding, Yong, Tang, Shijie, Wang, Yujue
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024 Springer Nature B.V SpringerOpen
Predmet:	Advanced Blockchain and Federated Learning Techniques Towards Secure Cloud Computing Computer Communication Networks Computer Science Computer System Implementation Computer Systems Organization and Communication Networks Cryptography Data collection Distributed homomorphic encryption Federated learning Information Systems Applications (incl.Internet) Low-quality data Machine learning Privacy Privacy protection Security Software Engineering/Programming and Operating Systems Special Purpose and Application-Based Systems Privacy protection Low-quality data Federated learning Distributed homomorphic encryption
ISSN:	2192-113X, 2192-113X
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