7 citations · 19 across the 7 of their papers we have counts for
4 papers · 2 filters
Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
Yu Jiang, Xindi Tong, Ziyao Liu +3
Federated unlearning (FU) offers a promising solution to effectively address the need to erase the impact of specific clients' data on the global model in federated learning (FL),…
Guaranteeing Data Privacy in Federated Unlearning with Dynamic User Participation
Ziyao Liu, Yu Jiang, Weifeng Jiang +3
Federated Unlearning (FU) is gaining prominence for its capability to eliminate influences of Federated Learning (FL) users' data from trained global FL models. A straightforward F…
Privacy-Preserving Federated Unlearning with Certified Client Removal
Ziyao Liu, Huanyi Ye, Yu Jiang +4
In recent years, Federated Unlearning (FU) has gained attention for addressing the removal of a client's influence from the global model in Federated Learning (FL) systems, thereby…
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
Yu Jiang, Jiyuan Shen, Ziyao Liu +2
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detectin…