Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Zhou, Pengyang"'
Autor:
Liao, Xinting, Liu, Weiming, Zhou, Pengyang, Yu, Fengyuan, Xu, Jiahe, Wang, Jun, Wang, Wenjie, Chen, Chaochao, Zheng, Xiaolin
Federated learning (FL) is a promising machine learning paradigm that collaborates with client models to capture global knowledge. However, deploying FL models in real-world scenarios remains unreliable due to the coexistence of in-distribution data
Externí odkaz:
http://arxiv.org/abs/2410.11397
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Yu, Fengyuan, Zhu, Huabin, Yao, Binhui, Wang, Tao, Zheng, Xiaolin, Tan, Yanchao
Federated learning achieves effective performance in modeling decentralized data. In practice, client data are not well-labeled, which makes it potential for federated unsupervised learning (FUSL) with non-IID data. However, the performance of existi
Externí odkaz:
http://arxiv.org/abs/2403.16398
Autor:
Liao, Xinting, Chen, Chaochao, Liu, Weiming, Zhou, Pengyang, Zhu, Huabin, Shen, Shuheng, Wang, Weiqiang, Hu, Mengling, Tan, Yanchao, Zheng, Xiaolin
Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the mode
Externí odkaz:
http://arxiv.org/abs/2308.11646
Autor:
Liao, Xinting, Liu, Weiming, Chen, Chaochao, Zhou, Pengyang, Zhu, Huabin, Tan, Yanchao, Wang, Jun, Qi, Yue
Federated learning (FL) collaboratively models user data in a decentralized way. However, in the real world, non-identical and independent data distributions (non-IID) among clients hinder the performance of FL due to three issues, i.e., (1) the clas
Externí odkaz:
http://arxiv.org/abs/2307.14384
Autor:
Xu, Da, Li, Caifei, Huang, Yao, Hu, Kaixin, Wang, Cheng, Zhou, Pengyang, Shen, Haiying, Liu, Chang, Xu, Jiatong, He, Jinyuan, Jiang, Jiaxu, Qi, Qi, Guo, Yu, Pan, Xiaoyan
Publikováno v:
In Theriogenology 15 January 2025 232:1-8
Publikováno v:
In Computers and Chemical Engineering January 2025 192
Encoder-decoder-based recurrent neural network (RNN) has made significant progress in sequence-to-sequence learning tasks such as machine translation and conversational models. Recent works have shown the advantage of this type of network in dealing
Externí odkaz:
http://arxiv.org/abs/2108.11763
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Zhou, Pengyang, Xiao, Nan, Wang, Jian, Wang, Zhanhuai, Zheng, Shuchun, Shan, Siyang, Wang, Jianping, Du, Jinlin, Wang, Jianwei
Publikováno v:
In Cancer Letters 28 January 2017 385:39-45
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.