PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
arXiv:2606.09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency. Unlike random instance-wise missingness, a deficient client lacks the local semantic basis needed to reconstruct the absent modality. More
The increasing complexity of multimodal AI and distributed data environments necessitates new technical solutions for data incompleteness, especially in federated learning where data cannot be centralized.
This development addresses a fundamental challenge in federated learning and multimodal AI, potentially unlocking broader applications for privacy-preserving AI across diverse, incomplete datasets.
The ability to impute missing modalities in a topology-aware manner for federated graph learning means AI models can be trained more effectively on real-world, heterogeneously structured, and incomplete data, expanding the reach of collaborative AI.
- · AI researchers
- · Federated learning platforms
- · Sectors with decentralized multimodal data (e.g., healthcare, retail)
- · Secure AI applications
- · Traditional centralized AI approaches
- · Companies relying on complete, homogeneous datasets
Improved performance and broader applicability of federated learning systems dealing with multimodal data.
Acceleration of privacy-preserving AI deployments in industries where data is siloed and incomplete by design.
Enhanced collaboration and knowledge sharing across organizations without direct data harmonization, leading to new AI-driven service models.
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