Výsledky vyhledávání - "Xu, Shuangqing"

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Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy

Autor: Xu, Shuangqing, Zheng, Yifeng, Hua, Zhongyun

Federated learning (FL) has rapidly become a compelling paradigm that enables multiple clients to jointly train a model by sharing only gradient updates for aggregation, without revealing their local private data. In order to protect the gradient upd

Externí odkaz: http://arxiv.org/abs/2410.03407

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Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables

Autor: Zheng, Yifeng, Xu, Shuangqing, Wang, Songlei, Gao, Yansong, Hua, Zhongyun

Vertical federated learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VF

Externí odkaz: http://arxiv.org/abs/2305.12652

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Akademický článek

Development Patterns and Paths of China’s Energy Internet Industry

Autor: Xu Shuangqing, Zhang Zhe, Zhang Xuan

Publikováno v: 中国工程科学, Vol 26, Iss 3, Pp 164-175 (2024)

Energy Internet is crucial for promoting energy transition and revolution, and it has achieved multi-dimensional development in terms of technology, system, and industry. The development of energy Internet involves a wide range of aspects including t

Externí odkaz: https://doaj.org/article/a7a433336a1a4eca991cb66d5ac180bd