Disagreement-Aware Subgraph Federated Learning via Uncertainty-Guided Local-Global Alignment
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.