9 citations · 20 across the 6 of their papers we have counts for
6 papers
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 and Optimizers for Adversarial Robustness
Hengyue Liang, Buyun Liang, Le Peng +3
Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algo…
Optimization for Robustness Evaluation beyond Metrics
Hengyue Liang, Buyun Liang, Ying Cui +2
Empirical evaluation of deep learning models against adversarial attacks entails solving nontrivial constrained optimization problems. Popular algorithms for solving these constrai…
Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning
Qi Wang, Ying Cui, Chenglin Li +2
Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all mod…
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…
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…