6 citations · 8 across the 10 of their papers we have counts for
11 papers
AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning
Shengyang Li, Yiting Dong, Liuyang Song +5
Federated learning enables collaborative model training across distributed edge devices while strictly preserving data privacy. To facilitate practical deployment on resource-const…
FedSRD: Sparsify-Reconstruct-Decompose for Communication-Efficient Federated Large Language Models Fine-Tuning
Guochen Yan, Luyuan Xie, Qingni Shen +2
The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaus…
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
Luyuan Xie, Tianyu Luan, Wenyuan Cai +7
Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, exist…
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Guochen Yan, Luyuan Xie, Xinyi Gao +4
Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distr…
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
Guochen Yan, Xunkai Li, Luyuan Xie +3
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distribu…
Divide and Fuse: Body Part Mesh Recovery from Partially Visible Human Images
Tianyu Luan, Zhongpai Gao, Luyuan Xie +8
We introduce a novel bottom-up approach for human body mesh reconstruction, specifically designed to address the challenges posed by partial visibility and occlusion in input image…
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
Luyuan Xie, Manqing Lin, ChenMing Xu +7
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates colla…
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
Luyuan Xie, Manqing Lin, Siyuan Liu +6
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity an…
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Luyuan Xie, Manqing Lin, Tianyu Luan +4
Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network arch…
SHISRCNet: Super-resolution And Classification Network For Low-resolution Breast Cancer Histopathology Image
Luyuan Xie, Cong Li, Zirui Wang +4
The rapid identification and accurate diagnosis of breast cancer, known as the killer of women, have become greatly significant for those patients. Numerous breast cancer histopath…
TC-SKNet with GridMask for Low-complexity Classification of Acoustic scene
Luyuan Xie, Yan Zhong, Lin Yang +3
Convolution neural networks (CNNs) have good performance in low-complexity classification tasks such as acoustic scene classifications (ASCs). However, there are few studies on the…