30 citations · 112 across the 17 of their papers we have counts for
13 papers · 1 filter
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…
Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination
Ming Hu, Zhihao Yue, Xiaofei Xie +6
Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Fede…
A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold
Yanhong Fei, Xian Wei, Yingjie Liu +2
Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e.g., feature redundancy, and vanishing…
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Pengyu Zhang, Yingbo Zhou, Ming Hu +2
Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Si…
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…
FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation
Ming Hu, Peiheng Zhou, Zhihao Yue +6
As a promising distributed machine learning paradigm, Federated Learning (FL) has attracted increasing attention to deal with data silo problems without compromising user privacy.…
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…
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
Jianzhang Zheng, Fan Yang, Hao Shen +4
Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential neg…
FedCAT: Towards Accurate Federated Learning via Device Concatenation
Ming Hu, Tian Liu, Zhiwei Ling +2
As a promising distributed machine learning paradigm, Federated Learning (FL) enables all the involved devices to train a global model collaboratively without exposing their local…
Towards Fast and Accurate Federated Learning with non-IID Data for Cloud-Based IoT Applications
Tian Liu, Jiahao Ding, Ting Wang +2
As a promising method of central model training on decentralized device data while securing user privacy, Federated Learning (FL)is becoming popular in Internet of Things (IoT) des…