7 citations · 19 across the 7 of their papers we have counts for
7 papers
Certifying the Right to Be Forgotten: Primal-Dual Optimization for Sample and Label Unlearning in Vertical Federated Learning
Yu Jiang, Xindi Tong, Ziyao Liu +3
Federated unlearning has become an attractive approach to address privacy concerns in collaborative machine learning, for situations when sensitive data is remembered by AI models…
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),…
FedUHB: Accelerating Federated Unlearning via Polyak Heavy Ball Method
Yu Jiang, Chee Wei Tan, Kwok-Yan Lam
Federated learning facilitates collaborative machine learning, enabling multiple participants to collectively develop a shared model while preserving the privacy of individual data…
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…
A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Ziyao Liu, Yu Jiang, Jiyuan Shen +4
In recent years, the notion of ``the right to be forgotten" (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanism…