4 citations · 5 across the 5 of their papers we have counts for
5 papers
FedTopo: Topology-Informed Representation Alignment in Federated Learning under Non-I.I.D. Conditions
Ke Hu, Liyao Xiang, Peng Tang +1
Current federated-learning models deteriorate under heterogeneous (non-I.I.D.) client data, as their feature representations diverge and pixel- or patch-level objectives fail to ca…
FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning
Yubin Zheng, Pak-Hei Yeung, Jing Xia +4
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without exposing local data, balancing performance and privacy. However, domain sh…
PassTSL: Modeling Human-Created Passwords through Two-Stage Learning
Yangde Wang, Haozhang Li, Weidong Qiu +2
Textual passwords are still the most widely used user authentication mechanism. Due to the close connections between textual passwords and natural languages, advanced technologies…
Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency
Yubin Zheng, Peng Tang, Tianjie Ju +2
Medical image segmentation plays a vital role in clinic disease diagnosis and medical image analysis. However, labeling medical images for segmentation task is tough due to the ind…
Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights
Ke Hu, WeiDong Qiu, Peng Tang
In the field of federated learning, addressing non-independent and identically distributed (non-i.i.d.) data remains a quintessential challenge for improving global model performan…