6 citations · 8 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…