Boosting privately: Federated extreme gradient boosting for mobile crowdsensing

Boosting privately: Federated extreme gradient boosting for mobile crowdsensing

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key...

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書目詳細資料
Main Authors:	LIU, Yang, MA, Zhuo, LIU, Ximeng, MA, Siqi, NEPAL, Surya, DENG, Robert H., REN, Kui
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 2020
主題:	Privacy-Preserving Federated learning Extreme gradient boosting Mobile crowdsensing Information Security
在線閱讀:	https://ink.library.smu.edu.sg/sis_research/5921 https://ink.library.smu.edu.sg/context/sis_research/article/6924/viewcontent/BoostingPrivately_av.pdf
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

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