MMiC combines one-layer parameter substitution, Banzhaf-inspired client selection, and Markowitz-inspired global aggregation to report top global and personalized results on multiple multimodal federated learning benchmarks with missing modalities.
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MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning
MMiC combines one-layer parameter substitution, Banzhaf-inspired client selection, and Markowitz-inspired global aggregation to report top global and personalized results on multiple multimodal federated learning benchmarks with missing modalities.