29 citations · 52 across the 10 of their papers we have counts for
10 papers
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
Ruixuan Liu, Ming Hu, Zeke Xia +5
Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive m…
Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learning
Dengke Yan, Ming Hu, Zeke Xia +4
Due to its advantages in resource constraint scenarios, Split Federated Learning (SFL) is promising in AIoT systems. However, due to data heterogeneity and stragglers, SFL suffers…
WaveAttack: Asymmetric Frequency Obfuscation-based Backdoor Attacks Against Deep Neural Networks
Jun Xia, Zhihao Yue, Yingbo Zhou +3
Due to the popularity of Artificial Intelligence (AI) technology, numerous backdoor attacks are designed by adversaries to mislead deep neural network predictions by manipulating t…
FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning
Yanxin Yang, Ming Hu, Xiaofei Xie +4
As a distributed machine learning paradigm, Federated Learning (FL) enables large-scale clients to collaboratively train a model without sharing their raw data. However, due to the…
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation
Jun Xia, Yi Zhang, Zhihao Yue +3
Federated learning (FL) has been recognized as a privacy-preserving distributed machine learning paradigm that enables knowledge sharing among various heterogeneous artificial inte…
GitFL: Adaptive Asynchronous Federated Learning using Version Control
Ming Hu, Zeke Xia, Zhihao Yue +4
As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AI…
FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment
Zhiwei Ling, Zhihao Yue, Jun Xia +3
Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distribu…
Model-Contrastive Learning for Backdoor Defense
Zhihao Yue, Jun Xia, Zhiwei Ling +4
Due to the popularity of Artificial Intelligence (AI) techniques, we are witnessing an increasing number of backdoor injection attacks that are designed to maliciously threaten Dee…
Eliminating Backdoor Triggers for Deep Neural Networks Using Attention Relation Graph Distillation
Jun Xia, Ting Wang, Jiepin Ding +2
Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs).Although the state-of-th…
Efficient Federated Learning for AIoT Applications Using Knowledge Distillation
Tian Liu, Zhiwei Ling, Jun Xia +3
As a promising distributed machine learning paradigm, Federated Learning (FL) trains a central model with decentralized data without compromising user privacy, which has made it wi…