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...

Saved in:

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

相似書籍

A blockchain-based location privacy-preserving crowdsensing system
由: YANG, Mengmeng, et al.
出版: (2019)

CROWDFL: Privacy-preserving mobile crowdsensing system via federated learning
由: ZHAO, Bowen, et al.
出版: (2023)

Gradient boosted graph convolutional network on heterophilic graph
由: Seah, Ming Yang
出版: (2024)

CrowdFA: A privacy-preserving mobile crowdsensing paradigm via federated analytics
由: ZHAO, Bowen, et al.
出版: (2023)

PRICE: Privacy and reliability-aware real-time incentive system for crowdsensing
由: ZHAO, Bowen, et al.
出版: (2021)

INFLATION AND OUTPUT GROWTH FORECASTING IN A DATA-RICH ENVIRONMENT USING EXTREME GRADIENT BOOSTED TREES WITH ONLINE LEARNING
由: LIM KAI ZHUO
出版: (2021)

Multi-round efficient and secure truth discovery in mobile crowdsensing systems
由: HU, Chenfei, et al.
出版: (2024)

PACE: Privacy-preserving and quality-aware incentive mechanism for mobile crowdsensing
由: ZHAO, Bowen, et al.
出版: (2020)

Reliable and privacy-preserving truth discovery for mobile crowdsensing systems
由: ZHANG, Chuan, et al.
出版: (2019)

Efficient gradient boosted decision tree training on GPUs
由: Zeyi Wen, et al.
出版: (2020)

Hercules: Boosting the performance of privacy-preserving federated learning
由: XU, Guowen, et al.
出版: (2023)

Lightweight privacy-preserving ensemble classification for face recognition
由: MA, Zhuo, et al.
出版: (2019)

Semi-supervised federated heterogeneous transfer learning
由: Feng, Siwei, et al.
出版: (2022)

REMOVE OUTLIERS AND USE ENSEMBLES OF GRADIENT BOOSTING TREES: LESSONS LEARNED FROM THE ASHRAE GREAT ENERGY PREDICTOR III KAGGLE COMPETITION
由: LIU HAO
出版: (2021)

Privacy-preserving user recruitment with sensing quality evaluation in mobile crowdsensing
由: AN, Jieying, et al.
出版: (2024)

Catch you if you deceive me: Verifiable and privacy-aware truth discovery in crowdsensing systems
由: XU, Guowen, et al.
出版: (2020)

Dynamic performance improvement in boost and buck-boost-derived power electronic converters
由: KANAKASABAI VISWANATHAN
出版: (2010)

Lightweight privacy-preserving cross-cluster federated learning with heterogeneous data
由: CHEN, Zekai, et al.
出版: (2024)

Ensemble boosting in complex environment and its applications in facial detection and identification
由: LIU JIANG, JIMMY
出版: (2010)

Privacy-preserving asynchronous federated learning under non-IID settings
由: MIAO, Yinbin, et al.
出版: (2024)

An anonymous reputation management system for mobile crowdsensing based on dual blockchain
由: WU, Hao-Tian, et al.
出版: (2022)

A new formulation of gradient boosting
由: Wozniakowski, Alex, et al.
出版: (2022)

Privacy-enhancing and robust backdoor defense for federated learning on heterogeneous data
由: CHEN, Zekai, et al.
出版: (2024)

Blockchain-based privacy-preserving federated learning for mobile crowdsourcing
由: Ma, Haiying, et al.
出版: (2023)

Privacy and robustness in federated learning: attacks and defenses
由: Lyu, Lingjuan, et al.
出版: (2023)

Towards privacy-preserving spatial distribution crowdsensing: A game theoretic approach
由: REN, Yanbing, et al.
出版: (2022)

A hybrid crowdsensing approach with cloud-edge computing framework for design innovation in smart product-service systems
由: Zheng, Pai, et al.
出版: (2021)

Implementation and control of a transformer-less quadratic boost converter
由: Wong, Jun Bo
出版: (2024)

A socially-aware incentive mechanism for mobile crowdsensing service market
由: Nie, Jiangtian, et al.
出版: (2019)

An integrated marketing communications campaign for Bonakid Choco Boost
由: Dizon, Laureen S., et al.
出版: (2013)

DESIGN METHODOLOGIES FOR TRUSTED AND EFFICIENT OUTSOURCING OF PRIVACY-PRESERVING AI
由: MEFTAH SOUHAIL
出版: (2023)

`Extreme communicative acts' and the boosting of illocutionary force
由: Wee, L.
出版: (2011)

Inductor design for a buck boost converter
由: Yeo, Yee Jie
出版: (2024)

ShieldFL: Mitigating model poisoning attacks in privacy-preserving federated learning
由: MA, Zhuoran, et al.
出版: (2022)

A Stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing
由: Nie, Jiangtian, et al.
出版: (2020)

Stochastic geometry-based age of information performance analysis for privacy preservation-oriented mobile crowdsensing
由: Yang, Yaoqi, et al.
出版: (2023)

Practical federated gradient boosting decision trees
由: Li, Q, et al.
出版: (2022)

Dynamic response improvement of a bidirectional interleaved buck-boost converter
由: Lei, Yihan
出版: (2024)

Gradient boosting machines, a tutorial
由: Natekin, A, et al.
出版: (2020)

A novel tri-state boost converter with fast dynamics
由: Viswanathan, K., et al.
出版: (2014)