FedFST is a spectral-aware framework that mitigates dual spectral forgetting in Federated Graph Continual Learning, and comprises Historical High-Frequency Knowledge Restoration to reconstruct and preserve high-frequency inconsistency knowledge, and Historical Low-Frequency Semantic Transfer to stabilize low-frequency...
Hanyao Guo, Zihan Tan, Wen-Ke Huang et al.· Proceedings of the 32nd ACM...· 0 citations
Federated Graph Learning (FGL) enables privacy-preserving GNN training over distributed graph data, yet dynamic task streams in Federated Graph Continual Learning (FGCL) inevitably lead to catastrophic forgetting. From a spectral perspective, this forgetting manifests as two fundamental challenges: high-frequency incon...
Hanyao Guo, Zihan Tan, Wenke Huang et al.· Proceedings of the 32nd ACM...· 0 citations
A Transformer-based baseline framework for visible-infrared ReID is proposed, designed to effectively capture modality-invariant features and outline several promising directions for future research.
Xiao Wang, Bing Wang, Bin Yang et al.· arXiv.org· 0 citations
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