Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Luo, Jiahuan"'
Autor:
Gu, Hanlin, Luo, Jiahuan, Kang, Yan, Yao, Yuan, Zhu, Gongxi, Li, Bowen, Fan, Lixin, Yang, Qiang
Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific condition
Externí odkaz:
http://arxiv.org/abs/2406.01085
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inferenc
Externí odkaz:
http://arxiv.org/abs/2301.12623
Federated learning (FL) has emerged as a practical solution to tackle data silo issues without compromising user privacy. One of its variants, vertical federated learning (VFL), has recently gained increasing attention as the VFL matches the enterpri
Externí odkaz:
http://arxiv.org/abs/2209.03885
Vertical federated learning (VFL), a variant of Federated Learning (FL), has recently drawn increasing attention as the VFL matches the enterprises' demands of leveraging more valuable features to achieve better model performance. However, convention
Externí odkaz:
http://arxiv.org/abs/2208.08934
Autor:
Xu, Jia, Zhang, Xiaoyu, Sun, Shixiong, Fu, Rong, Cheng, Fangyuan, Wei, Peng, Luo, Jiahuan, Li, Qing, Fang, Chun, Lin, He, Han, Jiantao
Publikováno v:
In Journal of Colloid And Interface Science September 2024 669:877-885
Autor:
Li, Shuo, Luo, Jiahuan, Wang, Jing, Zhu, Yue, Feng, Jingkang, Fu, Ning, Wang, Hao, Guo, Yao, Tian, Dayong, Zheng, Yong, Sun, Shixiong, Zhang, Chuanxiang, Chen, Kongyao, Mu, Shichun, Huang, Yunhui
Publikováno v:
In Journal of Colloid And Interface Science September 2024 669:265-274
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based p
Externí odkaz:
http://arxiv.org/abs/2111.08211
Autor:
Zhu, Yue, Li, Shuo, Fu, Ning, Wang, Hao, Tian, Dayong, Zheng, Yong, Wang, Jing, Zhang, Chuanxiang, Mu, Shichun, Luo, Jiahuan
Publikováno v:
In Journal of Electroanalytical Chemistry 15 March 2024 957
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties. In the training process of FL, no party has a global view of data distributions or
Externí odkaz:
http://arxiv.org/abs/2101.11896
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