Dual-Partial Multimodal Federated Learning via Uncertainty-Gated Modality–Label Evidence Graphs

Multimodal federated learning (MFL) becomes particularly difficult when modalities and labels are both partially observed: missing modalities make cross-modal transfer necessary, whereas missing labels remove the supervision needed to verify whether that transfer is correct. This evidence dilemma makes the most transfer-dependent targets the least directly verifiable. We formulate dual-partial MFL as progressively verified evidence transfer and propose Dual-Partial Evidence-Graph Federated Learning (DualP-EGFL). DualP-EGFL constructs signed modality–label evidence prototypes from verified observations, admits a cross-modal relation only after checking structural support and verified target-side utility, and applies accepted relations to an unknown target only when pair-specific out-of-distribution (OOD) gating and conservative Top-$K$ path aggregation provide sufficient support. Unsupported targets remain low-evidence rather than receiving fabricated pseudo-labels. Across benchmark settings, DualP-EGFL improves supported-hidden Macro-AUPRC by up to 7.65 percentage points over recent MFL baselines, with the largest gain in the 100-client MM-IMDb setting; in calibrated smaller-client settings, it also reduces false support relative to ungated graph transfer.