125 citations · 173 across the 13 of their papers we have counts for
21 papers
Joint Coreset Construction and Quantization for Distributed Machine Learning
Hanlin Lu, Changchang Liu, Shiqiang Wang +4
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication…
KerGNNs: Interpretable Graph Neural Networks with Graph Kernels
Aosong Feng, Chenyu You, Shiqiang Wang +1
Graph kernels are historically the most widely-used technique for graph classification tasks. However, these methods suffer from limited performance because of the hand-crafted com…
Cost-Effective Federated Learning in Mobile Edge Networks
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of mobile devices to collaboratively learn a model under the coordination of a central server…
Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
Yiwen Han, Shihao Shen, Xiaofei Wang +2
Kubernetes (k8s) has the potential to merge the distributed edge and the cloud but lacks a scheduling framework specifically for edge-cloud systems. Besides, the hierarchical distr…
Cost-Effective Federated Learning Design
Bing Luo, Xiang Li, Shiqiang Wang +2
Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its pract…
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Pengchao Han, Jihong Park, Shiqiang Wang +1
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representatio…