7 citations · 13 across the 8 of their papers we have counts for
8 papers
Smart Sampling: Helping from Friendly Neighbors for Decentralized Federated Learning
Lin Wang, Yang Chen, Yongxin Guo +1
Federated Learning (FL) is gaining widespread interest for its ability to share knowledge while preserving privacy and reducing communication costs. Unlike Centralized FL, Decentra…
Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing
Zhe Kong, Wentian Zhang, Tao Wang +4
Face recognition systems have raised concerns due to their vulnerability to different presentation attacks, and system security has become an increasingly critical concern. Althoug…
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
Lin Wang, Zhichao Wang, Xi Leng +1
Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protec…
MyoFold: rapid Myocardial tissue and movement quantification via a highly Folded sequence
Rui Guo, Yingwei Fan, Bowei Liu +7
Purpose: To develop and evaluate a cardiovascular magnetic resonance sequence (MyoFold) for rapid myocardial tissue and movement characterization. Method: MyoFold sequentially perf…
MProtoNet: A Case-Based Interpretable Model for Brain Tumor Classification with 3D Multi-parametric Magnetic Resonance Imaging
Yuanyuan Wei, Roger Tam, Xiaoying Tang
Recent applications of deep convolutional neural networks in medical imaging raise concerns about their interpretability. While most explainable deep learning applications use post…
Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation
Li Lin, Jiewei Wu, Yixiang Liu +2
Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. The statistical heterogeneity (e.g., no…