arXiv Machine Learning July 1, 2026 By Zekai Chen, Kairui Yang, Xuaner Chen, Xunkai Li, Xun Wu, Rong-Hua Li, Guoren Wang

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby supporting diverse graph-centric and modality-centric tasks.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 20

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

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.

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fine-tuning multimodal safety

arXiv Machine Learning

Aug 4

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

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By Yinlin Zhu, Di Wu, Yi Zhang, Xunkai Li, Wang Luo, Wei-Jin Huang, Miao Hu, Guocong Quan

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arXiv Machine Learning

Jul 31

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

arXiv:2607. 27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information.

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arXiv Machine Learning

Jul 28

FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities

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By Haochen Liang, Jie Zhang, Hideya Ochiai

multimodal safety

arXiv Machine Learning

Aug 3

MMFGU: Multimodal Federated Graph Unlearning

arXiv:2607. 28708v1 Announce Type: new Abstract: Multimodal federated graph learning enables clients to collaboratively train graph models over structural, textual, and visual signals without sharing private local data.

By Haodong Lu, Zekai Chen, Weiwei Ji, Shihao Li, Xunkai Li, Xun Wu, Yinlin Zhu, Rong-Hua Li

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arXiv AI

Jul 24

Semi-Supervised Text-Attributed Graph Distillation

arXiv:2607. 20477v1 Announce Type: new Abstract: {\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics.

By Yurui Lai, Samir Moustafa, Renchi Yang, Tsz Nam Chan

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Related stories

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

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

FedTaste: Topology-Aware Structural Transfer for Multimodal Federated Learning with Missing Modalities

MMFGU: Multimodal Federated Graph Unlearning

Semi-Supervised Text-Attributed Graph Distillation