Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework
Multimodal-attributed graphs (MAGs), whose nodes carry modalities such as images and text alongside topological structure, now pervade applications including social platforms, e-commerce, and biomedical networks, offering richer semantic signals than single-modality graphs. In practice, such graphs are fragmented across privacy-restricted silos owned by different platforms and institutions, so learning a broadly transferable model over them demands collaborative training that never exposes raw d
Record details
Published: 17 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Privacy
Retrieved: 20 July 2026
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ethics.ai (17 July 2026), “Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework,” evidence record 11878, https://ethics.ai/record/11878 (originally published by arXiv cs.LG).
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