20 citations · 56 across the 8 of their papers we have counts for
6 papers · 1 filter
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
Li Shen, Yan Sun, Zhiyuan Yu +3
The field of deep learning has witnessed significant progress, particularly in computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models tr…
Visual Prompt Based Personalized Federated Learning
Guanghao Li, Wansen Wu, Yan Sun +3
As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowle…
Subspace based Federated Unlearning
Guanghao Li, Li Shen, Yan Sun +3
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data. Federated unlearning is an inverse FL proces…
Fusion of Global and Local Knowledge for Personalized Federated Learning
Tiansheng Huang, Li Shen, Yan Sun +2
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data. However, it is still inconcl…
Improving the Model Consistency of Decentralized Federated Learning
Yifan Shi, Li Shen, Kang Wei +4
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its…
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization
Yan Sun, Yi Han, Jicong Fan
Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding…