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arXiv · 2601.22416

MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning

Xunkai Li·Yuming Ai·Yinlin Zhu·Haodong Lu·Yi Zhang·Guohao Fu·Bowen Fan·Qiangqiang Dai·Rong-Hua Li·Guoren Wang
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Abstract

Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performance, MMAGs in real-world applications are often distributed across isolated platforms and cannot be shared due to privacy concerns or commercial constraints. Federated graph learning (FGL) offers a natural solution for collaborative training under such settings; however, existing studies largely focus on single-modality graphs and do not adequately address the challenges unique to multimodal federated graph learning (MMFGL). To bridge this gap, we present MM-OpenFGL, the first comprehensive benchmark that systematically formalizes the MMFGL paradigm and enables rigorous evaluation. MM-OpenFGL comprises 19 multimodal datasets spanning 7 application domains, 8 simulation strategies capturing modality and topology variations, 6 downstream tasks, and 57 state-of-the-art methods implemented through a modular API. Extensive experiments investigate MMFGL from the perspectives of necessity, effectiveness, robustness, and efficiency, offering valuable insights for future research on MMFGL.

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Xunkai Li, Yuming Ai, Yinlin Zhu, Haodong Lu, Yi Zhang, Guohao Fu, Bowen Fan, Qiangqiang Dai, Rong-Hua Li, Guoren Wang. 2026-01-29. MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning. https://arxiv.org/abs/2601.22416

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