Towards Robust Federated Multimodal Graph Learning under Modality Heterogeneity

ArXi:2605.12584v1 Announce Type: cross Recently, multimodal graph learning (MGL) has garnered significant attention for integrating diverse modality information and structured context to various network applications. However, real-world graphs are often isolated due to data-sharing limitations across multiple parties, and their modalities are frequently incomplete. This highlights an urgent need to develop a robust federated approach. However, we find that existing methods remain insufficient.