In NeurIPS 2022 Workshops on Decentralization and Trustworthy Machine Learning in Web3: Methodologies, Platforms,
In the Proceedings of the 2023 IEEE International Conference on Decentralized Machine Learning Systems. Honored with the Best Presentation Award.
In the Workshop on Privacy-Preserving Machine Learning at NeurIPS 2023. Awarded the Best Technical Demonstration.
Presented at the Conference on Trustworthy and Reliable AI (TRA) 2023. Runner-up in Best Application Paper.
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy.
Author: N. Dong, J. Sun, Z. Wang, S. Zhang, S. Zheng
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.
Author:N. Dong, Z. Wang, J. Sun, M. Kampffmeyer, W. Knottenbelt and E. Xing
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.
Author: Z. Wang, N. Dong, J. Sun, W. Knottenbelt and Y. Guo
With impressive achievements made, artificial intelligence is on the path forward to artificial general intelligence.
R Sun, Y Zhang, T Shah, J Sun, S Zhang, W Li, H Duan, B Wei, R Ranjan
Accurate prediction of future blood glucose (BG) levels can effectively improve BG management for people living with diabetes, thereby reducing complications and improving quality of life.
Chengzhe Piao, Taiyu Zhu, Stephanie E Baldeweg, Paul Taylor, Pantelis Georgiou, Jiahao Sun, Jun Wang, Kezhi Li
Preclinical drug safety assessment is a critical step in drug development that relies on time-consuming manual histopathological examination, which is prone to high inter-observer variability.
Zehua Cheng, Wei Dai, Jiahao Sun
One of the biggest challenges of building artificial intelligence (AI) model in healthcare area is the data sharing.
Rui Sun, Zhipeng Wang, Hengrui Zhang, Ming Jiang, Yizhe Wen, Jiqun Zhang, Jiahao Sun, Shuoying Zhang, Erwu Liu, Kezhi Li
Newly diagnosed Type 1 Diabetes (T1D) patients often struggle to obtain effective Blood Glucose (BG) prediction models due to the lack of sufficient BG data from Continuous Glucose Monitoring (CGM), presenting a significant "cold start" problem in patient care.
Chengzhe Piao, Taiyu Zhu, Yu Wang, Stephanie E Baldeweg, Paul Taylor, Pantelis Georgiou, Jiahao Sun, Jun Wang, Kezhi Li
