PRISM: New Framework for Handling Missing Data in Federated Multimodal Graph Learning
Researchers introduced PRISM, a new method for handling situations where some clients in a federated learning system lack certain types of data (like images or text). The framework uses topology-aware imputation to recover missing information from the broader network rather than relying solely on local data. The approach showed 4.48% average improvement over existing methods across six multimodal graph datasets.
PRISM addresses a practical challenge in multimodal federated graph learning where different clients may lack common data types—for example, one client might have image-interaction graphs but no text descriptions, while another has text but no images. Unlike random missing data at the instance level, this client-level modality deficiency means clients lack the local semantic foundation needed to reconstruct absent modalities. The researchers argue that in graph learning, incomplete representations can cause imputation errors to propagate, filter, and amplify through the network topology. PRISM solves this by proactively retrieving missing-modality semantics from the broader federation and introducing them into local graph propagation under topology-aware control. Experiments across six multimodal graph datasets and multiple task types demonstrated consistent improvements, with the method outperforming state-of-the-art baselines by an average of 4.48%.
What's missing
The paper's limitations, computational complexity analysis, scalability to very large federations, and potential failure modes are not detailed in the abstract provided.
What different sources said
- arXiv cs.LGCenter
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
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