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Arxiv
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Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
- Researchers propose a model for Federated Multimodal Knowledge Graph Completion (FedMKGC) to predict missing links in decentralized knowledge graphs without sharing sensitive information.
- The proposed framework, MMFeD3-HidE, addresses challenges such as incomplete entity embeddings and client heterogeneity in FedMKGC.
- MMFeD3-HidE consists of a Hyper-modal Imputation Diffusion Embedding model (HidE) for recovering multimodal distributions and Multimodal Federated Dual Distillation (MMFeD3) for transferring knowledge between clients and the server.
- Experiments on the proposed benchmark demonstrate the effectiveness, semantic consistency, and convergence robustness of MMFeD3-HidE for Federated Multimodal Knowledge Graph Completion.
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