197 citations · 370 across the 5 of their papers we have counts for
5 papers
DeceFL: A Principled Decentralized Federated Learning Framework
Ye Yuan, Jun Liu, Dou Jin +12
Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherent…
BoostTree and BoostForest for Ensemble Learning
Changming Zhao, Dongrui Wu, Jian Huang +4
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and…
A General End-to-end Diagnosis Framework for Manufacturing Systems
Ye Yuan, Guijun Ma, Cheng Cheng +4
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volum…
Data-driven Discovery of Cyber-Physical Systems
Ye Yuan, Xiuchuan Tang, Wei Pan +5
Cyber-physical systems (CPSs) embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, intelligent manufacture and medical…
Distributed Kalman filtering with minimum-time consensus algorithm
Ye Yuan, Ling Shi, Jun Liu +3
Fueled by applications in sensor networks, these years have witnessed a surge of interest in distributed estimation and filtering. A new approach is hereby proposed for the Distrib…