arXiv Machine Learning By Xunkai Li, Guohao Fu, Yuming Ai, Zhengyu Wu, Hongchao Qin, Rong-Hua Li, Guoren Wang

Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework

Read the original on arXiv Machine Learning →

arXiv:2607. 15687v1 Announce Type: new Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608. 00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependencies, have been widely adopted across diverse domains.

By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan
arXiv Machine Learning
23h ago

Coupling Perception and Reasoning in Federated Multimodal Graph Foundation Models

The paper introduces FedCORE, a federated adaptation framework for multimodal graph foundation models that jointly optimizes perception (Encoder) and reasoning (GNN) modules via a shared low‑dimensional latent state. Unlike prior methods that freeze the Encoder, FedCORE allows both components to adapt together, addressing the dependency between multimodal evidence extraction and graph‑based relational reasoning. Experiments show that FedCORE significantly narrows the Encoder–GNN pairing gap, achieving an 80.7% reduction compared to independent joint adaptation.

By Zekai Chen, Xun Wu, Hailin Zhang, Xunkai Li, Yu Liu, Kairui Yang, Muyan Huang, Xuaner Chen, Rong-Hua Li, Guoren Wang