7 papers
Beyond Normal References: Discriminative Few-Shot Anomaly Detection
Huan Wang, Jun Shen, Jun Yan +1
This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available a…
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…
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…
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…