Boosting privately: Federated extreme gradient boosting for mobile crowdsensing

• Main Authors: LIU, Yang, MA, Zhuo, LIU, Ximeng, MA, Siqi, NEPAL, Surya, DENG, Robert H., REN, Kui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Privacy-Preserving; Federated learning; Extreme gradient boosting; Mobile crowdsensing; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/5921; https://ink.library.smu.edu.sg/context/sis_research/article/6924/viewcontent/BoostingPrivately_av.pdf
• Institution: Singapore Management University

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...