4 papers
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
Shanshan Yan, Zexi Li, Chao Wu +4
Data heterogeneity, stemming from local non-IID data and global long-tailed distributions, is a major challenge in federated learning (FL), leading to significant performance gaps…
Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
Jiate Li, Meng Pang, Yun Dong +1
Graph neural networks (GNNs) are becoming the de facto method to learn on the graph data and have achieved the state-of-the-art on node and graph classification tasks. However, rec…
Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks
Jiate Li, Meng Pang, Yun Dong +2
Explaining Graph Neural Network (XGNN) has gained growing attention to facilitate the trust of using GNNs, which is the mainstream method to learn graph data. Despite their growing…
Practicable Black-box Evasion Attacks on Link Prediction in Dynamic Graphs -- A Graph Sequential Embedding Method
Jiate Li, Meng Pang, Binghui Wang
Link prediction in dynamic graphs (LPDG) has been widely applied to real-world applications such as website recommendation, traffic flow prediction, organizational studies, etc. Th…