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pro vyhledávání: '"Lei, Xinyu"'
Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is usually distributed and privacy sensitive. Multiple distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shar
Externí odkaz:
http://arxiv.org/abs/2307.13314
Federated learning (FL) supports distributed training of a global machine learning model across multiple devices with the help of a central server. However, data heterogeneity across different devices leads to the client model drift issue and results
Externí odkaz:
http://arxiv.org/abs/2207.11447
Publikováno v:
In Journal of Energy Storage 15 November 2024 102 Part A
Autor:
Zhang, Lizhai, Xu, Jiayuan, Yang, Xueling, Lei, Xinyu, Sun, Henghui, Huang, Yuhong, Lu, Hongbing, Ai, Taotao, Ma, Fei, Chu, Paul K.
Publikováno v:
In Sensors and Actuators: B. Chemical 15 November 2024 419
Publikováno v:
In Microchemical Journal September 2024 204
Autor:
Zhu, Qingsong, Lei, Xinyu, Zha, Xiaoqin, Ramadan Elsharkawy, Eman, Ren, Chenye, Chang, Huiyuan, El-Bahy, Salah M., Ren, Juanna, Wang, Renjie, El-Bahy, Zeinhom M., Guo, Zhanhu
Publikováno v:
In Composites Part A January 2025 188
Publikováno v:
In Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics December 2024 52
Publikováno v:
In Vacuum December 2024 230
Publikováno v:
In Talanta 1 December 2024 280
Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server. Clients do not need to submit their local data to the server during training, and hence the local training data of cli
Externí odkaz:
http://arxiv.org/abs/2111.10487