23 citations · 28 across the 5 of their papers we have counts for
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cs.LG2023★ 1 cited
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness
Zongxiong Chen, Jiahui Geng, Derui Zhu +5
The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset. It is a promising approach to accelerate neural network training and rela…
cs.LG2023
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Jiahui Geng, Zongxiong Chen, Yuandou Wang +5
Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high.…
cs.LG2021★ 23 cited
Towards General Deep Leakage in Federated Learning
Jiahui Geng, Yongli Mou, Feifei Li +4
Unlike traditional central training, federated learning (FL) improves the performance of the global model by sharing and aggregating local models rather than local data to protect…