Výsledky vyhledávání - "Yuan, Wenhao"

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Deep Generative Models-Assisted Automated Labeling for Electron Microscopy Images Segmentation

Autor: Yuan, Wenhao, Yao, Bingqing, Tan, Shengdong, You, Fengqi, He, Qian

The rapid advancement of deep learning has facilitated the automated processing of electron microscopy (EM) big data stacks. However, designing a framework that eliminates manual labeling and adapts to domain gaps remains challenging. Current researc

Externí odkaz: http://arxiv.org/abs/2407.19544

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QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy

Autor: Yuan, Wenhao, Wang, Xuehe

Federated Learning (FL) has increasingly been recognized as an innovative and secure distributed model training paradigm, aiming to coordinate multiple edge clients to collaboratively train a shared model without uploading their private datasets. The

Externí odkaz: http://arxiv.org/abs/2404.08261

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Characterization of an $\rm ^{27}Al^+$ ion optical clock laser with three independent methods

Autor: Wang, Zhiyuan, Ma, Zhiyu, Wei, Wenzhe, Chang, Jialu, Zhang, Jingxuan, Wu, Qiyue, Yuan, Wenhao, Deng, Ke, Lu, Zehuang, Zhang, Jie

We report on the development and performance evaluation of an ultra-stable clock laser for an $\rm ^{27}Al^+$ optical clock. The thermal noise limited ultra-stable laser is developed based on a 30 cm long ultra-stable cavity. Three independent evalua

Externí odkaz: http://arxiv.org/abs/2312.06133

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FedAgg: Adaptive Federated Learning with Aggregated Gradients

Autor: Yuan, Wenhao, Wang, Xuehe

Federated Learning (FL) has emerged as a crucial distributed training paradigm, enabling discrete devices to collaboratively train a shared model under the coordination of a central server, while leveraging their locally stored private data. Nonethel

Externí odkaz: http://arxiv.org/abs/2303.15799

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Adaptive Federated Learning via New Entropy Approach

Autor: Zheng, Shensheng, Yuan, Wenhao, Wang, Xuehe, Duan, Lingjie

Federated Learning (FL) has emerged as a prominent distributed machine learning framework that enables geographically discrete clients to train a global model collaboratively while preserving their privacy-sensitive data. However, due to the non-inde

Externí odkaz: http://arxiv.org/abs/2303.14966

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