1 citations · 1 across the 4 of their papers we have counts for
4 papers
Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Federated learning is a promising privacy-preserving paradigm for distributed machine learning. In this context, there is sometimes a need for a specialized process called machine…
Towards Efficient Target-Level Machine Unlearning Based on Essential Graph
Heng Xu, Tianqing Zhu, Lefeng Zhang +2
Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, ha…
Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs
Heng Xu, Tianqing Zhu, Lefeng Zhang +1
Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategie…
Don't Forget Too Much: Towards Machine Unlearning on Feature Level
Heng Xu, Tianqing Zhu, Wanlei Zhou +1
Machine unlearning enables pre-trained models to remove the effect of certain portions of training data. Previous machine unlearning schemes have mainly focused on unlearning a clu…