6 papers
HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
Zhao Su, Yuxin Xia, Haoran Li +4
Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions. However, assigning an independent functi…
FedHPro: Federated Hyper-Prototype Learning via Gradient Matching
Huan Wang, Jun Shen, Haoran Li +6
Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prototype-based FL is in the spo…
Learning to Fuse and Reconstruct Multi-View Graphs for Diabetic Retinopathy Grading
Haoran Li, Yuxin Lin, Huan Wang +9
Diabetic retinopathy (DR) is one of the leading causes of vision loss worldwide, making early and accurate DR grading critical for timely intervention. Recent clinical practices le…
FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning
Huan Wang, Haoran Li, Huaming Chen +3
Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity is…
FedSC: Federated Learning with Semantic-Aware Collaboration
Huan Wang, Haoran Li, Huaming Chen +3
Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving. However, one major challenge is the data heterogeneity issu…
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
Huan Wang, Haoran Li, Huaming Chen +5
With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for…