7 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.DC2024
DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learning
Kangyang Luo, Shuai Wang, Yexuan Fu +5
Federated Learning (FL) is a distributed machine learning scheme in which clients jointly participate in the collaborative training of a global model by sharing model information r…
cs.LG2024
Privacy-Preserving Federated Learning with Consistency via Knowledge Distillation Using Conditional Generator
Kangyang Luo, Shuai Wang, Xiang Li +3
Federated Learning (FL) is gaining popularity as a distributed learning framework that only shares model parameters or gradient updates and keeps private data locally. However, FL…
cs.CV2023★ 7 cited
DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning
Kangyang Luo, Shuai Wang, Yexuan Fu +3
Federated Learning (FL) is a privacy-constrained decentralized machine learning paradigm in which clients enable collaborative training without compromising private data. However,…