<ul><li>Multimodal Federated Learning (MFL) enhances efficiency and quality of multimodal learning by combining distributed data sources.</li><li>Missing modalities in MFL pose a significant challenge due to data quality issues or privacy policies.</li><li>MMiC is a framework designed to address modality incompleteness in MFL within clusters by replacing partial parameters in client models.</li><li>MMiC outperforms existing federated learning architectures in both global and personalized performance on multimodal datasets with missing modalities.</li></ul>

MMiC: Mitigating Modality Incompleteness in Clustered Federated Learning

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