DS-ADMM++: A Novel Distributed Quantized ADMM to Speed up Differentially Private Matrix Factorization

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent effici...

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Vydáno v:	IEEE transactions on parallel and distributed systems Ročník 33; číslo 6; s. 1289 - 1302
Hlavní autoři:	Zhang, Feng, Xue, Erkang, Guo, Ruixin, Qu, Guangzhi, Zhao, Gansen, Zomaya, Albert Y.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.06.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	ADMM Algorithms Charge coupled devices Costs Differential privacy Efficiency Factorization Machine learning Matrix decomposition Matrix factorization parallel and distributed computing Privacy quantization Quantization (signal) Recommender systems
ISSN:	1045-9219, 1558-2183
On-line přístup:	Získat plný text
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