no code implementations • 5 May 2024 • Yuanye Liu, Zheyao Gao, Nannan Shi, Fuping Wu, Yuxin Shi, Qingchao Chen, Xiahai Zhuang
MERIT enables uncertainty quantification of the predictions to enhance reliability, and employs a logic-based combination rule to improve interpretability.
no code implementations • 5 Jan 2024 • Yuxin Shi, Han Yu
Federated learning (FL) enables multiple data owners (a. k. a.
no code implementations • 20 Jul 2023 • Yuxin Shi, Zelei Liu, Zhuan Shi, Han Yu
By not using threshold-based reputation filtering, it provides FL clients with opportunities to redeem their reputations after a perceived poor performance, thereby further enhancing fair client treatment.
no code implementations • 21 Jun 2023 • Zheyao Gao, Yuanye Liu, Fuping Wu, Nannan Shi, Yuxin Shi, Xiahai Zhuang
Therefore, we propose a reliable multi-view learning method with interpretable combination rules, which can model global representations to improve the accuracy of predictions.
no code implementations • 2 Nov 2021 • Yuxin Shi, Han Yu, Cyril Leung
However, most current works focus on the interest of the central controller in FL, and overlook the interests of the FL clients.
1 code implementation • 10 Mar 2020 • Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi
The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.