6 citations · 10 across the 4 of their papers we have counts for
4 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…
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Ziwei Zhan, Wenkuan Zhao, Yuanqing Li +6
Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learnin…
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…