STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
Multimodal feder- ated learning via contrastive representation ensemble
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CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
RCSR is a personalization-friendly federated framework that improves cross-modal retrieval accuracy and stability under missing modalities via semantic routing and adapters.
citing papers explorer
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STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
STAGE builds a shared semantic space through feature translation and controlled graph propagation to reduce semantic drift in multimodal federated graph learning, delivering state-of-the-art results with lower communication cost.
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Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
CondI applies conditional diffusion models in a two-phase federated pipeline to impute within-modality missing data, then trains extractors on the completed inputs for downstream tasks on clinical datasets.
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Federated Cross-Modal Retrieval with Missing Modalities via Semantic Routing and Adapter Personalization
RCSR is a personalization-friendly federated framework that improves cross-modal retrieval accuracy and stability under missing modalities via semantic routing and adapters.