16 citations · 53 across the 14 of their papers we have counts for
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cs.LG2021★ 5 cited
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…
cs.LG2021★ 2 cited
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient
Sixing Yu, Phuong Nguyen, Ali Anwar +1
Federated Learning (FL) has emerged as a new paradigm for training machine learning models distributively without sacrificing data security and privacy. Learning models on edge dev…
cs.CV2021★ 7 cited
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
Sixing Yu, Arya Mazaheri, Ali Jannesari
Model compression is an essential technique for deploying deep neural networks (DNNs) on power and memory-constrained resources. However, existing model-compression methods often r…