4 citations · 5 across the 4 of their papers we have counts for
5 papers
Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation
Qi Guo, Zhen Tian, Minghao Yao +4
Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory r…
Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations
Qi Guo, Minghao Yao, Zhen Tian +4
Contribution evaluation in federated learning (FL) has become a pivotal research area due to its applicability across various domains, such as detecting low-quality datasets, enhan…
Dual Class-Aware Contrastive Federated Semi-Supervised Learning
Qi Guo, Yong Qi, Saiyu Qi +1
Federated semi-supervised learning (FSSL), facilitates labeled clients and unlabeled clients jointly training a global model without sharing private data. Existing FSSL methods pre…
FedMCSA: Personalized Federated Learning via Model Components Self-Attention
Qi Guo, Yong Qi, Saiyu Qi +2
Federated learning (FL) facilitates multiple clients to jointly train a machine learning model without sharing their private data. However, Non-IID data of clients presents a tough…
Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer
Qian Li, Qingyuan Hu, Yong Qi +3
Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. A…