Zobrazeno 1 - 10
of 4 134
pro vyhledávání: '"Sun, Sheng"'
Autor:
Jiang, Xuefeng, Sun, Sheng, Li, Jia, Xue, Jingjing, Li, Runhan, Wu, Zhiyuan, Xu, Gang, Wang, Yuwei, Liu, Min
Recently, federated learning (FL) has achieved wide successes for diverse privacy-sensitive applications without sacrificing the sensitive private information of clients. However, the data quality of client datasets can not be guaranteed since corres
Externí odkaz:
http://arxiv.org/abs/2408.04301
Federated Distillation (FD) offers an innovative approach to distributed machine learning, leveraging knowledge distillation for efficient and flexible cross-device knowledge transfer without necessitating the upload of extensive model parameters to
Externí odkaz:
http://arxiv.org/abs/2407.18039
Autor:
Dai, Shengnan, Zhang, Shijie, Sheng, Ye, Dong, Erting, Sun, Sheng, Xi, Lili, Snyder, G. Jeffrey, Xi, Jinyang, Yang, Jiong
Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point defect (PD) scattering term to the phonon relaxation time, which has bee
Externí odkaz:
http://arxiv.org/abs/2407.00308
Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges
Externí odkaz:
http://arxiv.org/abs/2405.13378
On-device intelligence (ODI) enables artificial intelligence (AI) applications to run on end devices, providing real-time and customized AI inference without relying on remote servers. However, training models for on-device deployment face significan
Externí odkaz:
http://arxiv.org/abs/2404.10255
The existing federated learning (FL) methods for spatio-temporal forecasting fail to capture the inherent spatio-temporal heterogeneity, which calls for personalized FL (PFL) methods to model the spatio-temporally variant patterns. While contrastive
Externí odkaz:
http://arxiv.org/abs/2404.03702
Autor:
Li, Zhi-Jun, Yuan, Ming-Kuan, Song, Yun-Xuan, Li, Yan-Gu, Li, Jing-Shu, Sun, Sheng-Sen, Wang, Xiao-Long, You, Zheng-Yun, Mao, Ya-Jun
Publikováno v:
Front. Phys. 19, 64201 (2024)
Modern particle physics experiments usually rely on highly complex and large-scale spectrometer devices. In high energy physics experiments, visualization helps detector design, data quality monitoring, offline data processing, and has great potentia
Externí odkaz:
http://arxiv.org/abs/2404.07951
Publikováno v:
IEEE Journal on Multiscale and Multiphysics Computational Techniques, 2024
Angular momenta of electromagnetic waves are important both in concepts and applications. In this work, we systematically discuss two types of angular momenta, i.e., spin angular momentum and orbital angular momentum in various cases, e.g., with sour
Externí odkaz:
http://arxiv.org/abs/2403.01504
Federated Distillation (FD) is a novel and promising distributed machine learning paradigm, where knowledge distillation is leveraged to facilitate a more efficient and flexible cross-device knowledge transfer in federated learning. By optimizing loc
Externí odkaz:
http://arxiv.org/abs/2401.03685
Autor:
Sun, Sheng-Yan, Li, Yu-Cheng, Chen, Shih-Hsuan, Wang, Kuan-Jou, Huang, Ching-Jui, Tsai, Tung-Ju, Kao, Wei-Ting, Hsu, Tzu-Liang, Li, Che-Ming
Fusing photon pairs creates an arena where indistinguishability can exist between two two-photon amplitudes contributing to the same joint photodetection event. This two-photon interference has been extensively utilized in creating multiphoton entang
Externí odkaz:
http://arxiv.org/abs/2401.03860