Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Chen, Shuaijun"'
Autor:
Nazemi, Niousha, Tavallaie, Omid, Chen, Shuaijun, Mandalari, Anna Maria, Thilakarathna, Kanchana, Holz, Ralph, Haddadi, Hamed, Zomaya, Albert Y.
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates.
Externí odkaz:
http://arxiv.org/abs/2409.01722
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as
Externí odkaz:
http://arxiv.org/abs/2408.08699
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offer
Externí odkaz:
http://arxiv.org/abs/2407.20573
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the serv
Externí odkaz:
http://arxiv.org/abs/2405.01144
Autor:
Chen, Shuaijun, Tavallaie, Omid, Hambali, Michael Henri, Zandavi, Seid Miad, Haddadi, Hamed, Lane, Nicholas, Guo, Song, Zomaya, Albert Y.
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual devices and constructs the global model on the server by performing mo
Externí odkaz:
http://arxiv.org/abs/2310.08147
Publikováno v:
In Colloids and Surfaces B: Biointerfaces January 2025 245
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distribution in the filtered output. To a
Externí odkaz:
http://arxiv.org/abs/2201.01503
Autor:
Huang, Aoling, Zhao, Yizhi, Guan, Feng, Zhang, Hongfeng, Luo, Bin, Xie, Ting, Chen, Shuaijun, Chen, Xinyue, Ai, Shuying, Ju, Xianli, Yan, Honglin, Yang, Lin, Yuan, Jingping
Publikováno v:
In Computational and Structural Biotechnology Journal December 2024 26:40-50
Autor:
Zhang, Wen Hui, Lau, Cher Chien, Sung, Yeong Yik, Zhou, WenLi, Jiang, Zhi Fei, Gao, Jin Wei, Chen, ShuaiJun, Mok, Wen Jye
Publikováno v:
In Comparative Immunology Reports December 2024 7
Autor:
Wu, Shaojie, Wang, Hua, Yang, Qixuan, Liu, Zhengyun, Du, Jingwen, Wang, Lei, Chen, Shuaijun, Lu, Qisi, Yang, Dong-Hua
Publikováno v:
In Cancer Letters 10 August 2024 597