FedDUA is proposed, a novel disagreement-aware and uncertainty-guided framework for subgraph FL that first models cross-client semantic disagreement via a lightweight semantic anchor graph and derives adaptive aggregation weights for reliable global federated knowledge.
Keao Xi, Nan-Nan Wu, Yi-Ming Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
LTRGAD is proposed, a two-stage GAD framework that performs feature selection based on local feature-topological residuals (LTR) and effectively introduces topological information while preserving the original local anomalous patterns, enabling more accurate local anomaly detection.
Ya-Zheng Zhao, Nan-Nan Wu, Hao Yin et al.· Proceedings of the Thirty-Fi...· 0 citations
Subgraph federated learning (subgraph FL) enables collaborative graph neural network training without sharing raw graph data, but suffers from severe Non-IID distributions and structural fragmentation. In such settings, Non-IID distributions induce pronounced client specialization, where each client excels in a subset...
Keao Xi, Nannan Wu, Yiming Zhao et al.· Proceedings of the 32nd ACM...· 0 citations
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