7 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning
Jiyuan Shen, Wenzhuo Yang, Zhaowei Chu +3
With the rapid development of low-cost consumer electronics and cloud computing, Internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed syst…
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…
AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories
Jiyuan Shen, Wenzhuo Yang, Kwok-Yan Lam
Training large AI models typically requires large-scale datasets in the machine learning process, making training and parameter-tuning process both time-consuming and costly. Some…
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…