MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908799057526784 |
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| author | Li, Xunkai Ai, Yuming Zhu, Yinlin Lu, Haodong Zhang, Yi Fu, Guohao Fan, Bowen Dai, Qiangqiang Li, Rong-Hua Wang, Guoren |
| author_facet | Li, Xunkai Ai, Yuming Zhu, Yinlin Lu, Haodong Zhang, Yi Fu, Guohao Fan, Bowen Dai, Qiangqiang Li, Rong-Hua Wang, Guoren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22416 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning Li, Xunkai Ai, Yuming Zhu, Yinlin Lu, Haodong Zhang, Yi Fu, Guohao Fan, Bowen Dai, Qiangqiang Li, Rong-Hua Wang, Guoren Machine Learning 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. |
| title | MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2601.22416 |