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FedHFT is proposed, an effective One-Shot Personalized Federated Graph Learning (OPFGL) framework that addresses this bottleneck from two perspectives, namely High-Fidelity Knowledge Externalization and Transferability-Ware Knowledge Integration.
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...
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...
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