LEAR: Evidence-Aware Local Learning with Missing Modalities

Learning with missing modalities requires a single model to predict from different observed subsets. Under block-local training, each module must learn from the available evidence without receiving gradients from later blocks. We introduce Layer-wise Evidence-Aware Representation Learning (LEAR), extending Forward-Forward-style local learning to prediction over every nonempty modality subset. LEAR turns the predictions of observed modality heads into a detached evidence reference for each sample. Within each block, this reference drives confidence-weighted distillation and coverage-bounded routing; across blocks, normalized detached representations preserve gradient locality. Evidence-Guided Inference (EGI) further combines fusion and available-evidence scores at prediction time. Compared with matched LocalCE, LEAR improves mean-subset Macro-F1 by 0.91 percentage points on WISDM. On CREMA-D, the actor-averaged mean-subset Macro-F1 gain is 9.79 points, including a 3.08-point contribution from evidence-aware training beyond EGI alone. Improvements hold across subject-disjoint folds, modality cardinalities, and 18 of 19 held-out CREMA-D actors. These results establish available evidence as an effective learning signal for coordinating gradient-isolated local modules under changing multimodal inputs.