9 citations · 11 across the 5 of their papers we have counts for
5 papers
RedFuser: An Automatic Operator Fusion Framework for Cascaded Reductions on AI Accelerators
Xinsheng Tang, Yangcheng Li, Nan Wang +5
Operator fusion, as a key performance optimization technique in the deployment of AI models, significantly improves execution efficiency and has been widely adopted in modern AI co…
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
Yangchen Li, Ying Cui, Vincent Lau
The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized…
Optimization-Based GenQSGD for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may hav…
An Optimization Framework for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remai…
Sample-based and Feature-based Federated Learning for Unconstrained and Constrained Nonconvex Optimization via Mini-batch SSCA
Ying Cui, Yangchen Li, Chencheng Ye
Federated learning (FL) has become a hot research area in enabling the collaborative training of machine learning models among multiple clients that hold sensitive local data. Neve…