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cs.LG2026
A Cross-graph Tuning-free GNN Prompting Framework
Yaqi Chen, Shixun Huang, Ryan Twemlow +6
GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter…
cs.LG2025
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…
cs.LG2025
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…