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

• URL: http://arxiv.org/abs/2601.22416v1
• Date: Thu, 29 Jan 2026 23:59:13 GMT
• Title: MM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
• Authors: Xunkai Li, Yuming Ai, Yinlin Zhu, Haodong Lu, Yi Zhang, Guohao Fu, Bowen Fan, Qiangqiang Dai, Rong-Hua Li, Guoren Wang,
• Abstract summary: Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures.<n> MM-OpenFGL is the first comprehensive benchmark that systematically formalizes the MMFGL paradigm and enables rigorous evaluation.<n> 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.
• Score: 33.909733872102656
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Toward Effective Multimodal Graph Foundation Model: A Divide-and-Conquer Based Approach [42.970648490410504]
  Multimodal Graph Foundation Models (MGFMs) allow for leveraging the rich multimodal information in Multimodal-Attributed Graphs (MAGs)<n>We propose PLANET, a novel framework employing a Divide-and-Conquer strategy to decouple modality interaction and alignment across distinct granularities.<n>We show that PLANET significantly outperforms state-of-the-art baselines across diverse graph-centric and multimodal generative tasks.
  arXiv  Detail & Related papers  (2026-02-04T01:05:12Z)
• Graph4MM: Weaving Multimodal Learning with Structural Information [52.16646463590474]
  Graphs provide powerful structural information for modeling intra- and inter-modal relationships.<n>Previous works fail to distinguish multi-hop neighbors and treat the graph as a standalone modality.<n>We propose Graph4MM, a graph-based multimodal learning framework.
  arXiv  Detail & Related papers  (2025-10-19T20:13:03Z)
• G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge [88.82814893945077]
  Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge.<n>Recent graph-enhanced RAG (GraphRAG) attempts to bridge this gap by constructing tailored graphs and enabling LLMs to reason on them.<n>G-reasoner is a unified framework that integrates graph and language foundation models for reasoning over diverse graph-structured knowledge.
  arXiv  Detail & Related papers  (2025-09-29T04:38:12Z)
• Graph Foundation Models: Bridging Language Model Paradigms and Graph Optimization [4.502753947356616]
  We introduce the Graph Foundation Model (GFM), the first framework capable of solving all distance-based optimization problems on graph structures.<n>GFM internalizes the graph's complex topological and neural rules, where the connectivity of the structure itself can be treated as the supervisory signal.<n>Our work establishes a new paradigm of adapting the pretrain-transfer framework to graph optimization, opening the door for applying foundation model innovations to Operations Research.
  arXiv  Detail & Related papers  (2025-09-29T04:05:48Z)
• UniGraph2: Learning a Unified Embedding Space to Bind Multimodal Graphs [34.48393396390799]
  We propose a novel cross-domain graph foundation model that enables general representation learning on multimodal graphs.<n>UniGraph2 employs modality-specific encoders alongside a graph neural network (GNN) to learn a unified low-dimensional embedding space.<n>We show that UniGraph2 significantly outperforms state-of-the-art models in tasks such as representation learning, transfer learning, and multimodal generative tasks.
  arXiv  Detail & Related papers  (2025-02-02T14:04:53Z)
• Knowledge-Driven Federated Graph Learning on Model Heterogeneity [47.98634086448171]
  Federated graph learning (FGL) has emerged as a promising paradigm for collaborative graph representation learning.<n>We propose the Federated Graph Knowledge Collaboration (FedGKC) framework to address the challenge of model-centric heterogeneous FGL.<n>FedGKC achieves an average accuracy gain of 3.74% over baselines in MHtFGL scenarios, while maintaining excellent performance in homogeneous settings.
  arXiv  Detail & Related papers  (2025-01-22T04:12:32Z)
• Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework [58.362064122489166]
  This paper introduces the Cross-modal Few-Shot Learning task, which aims to recognize instances across multiple modalities while relying on scarce labeled data.<n>We propose a Generative Transfer Learning framework by simulating how humans abstract and generalize concepts.<n>We show that the GTL achieves state-of-the-art performance across seven multi-modal datasets across RGB-Sketch, RGB-Infrared, and RGB-Depth.
  arXiv  Detail & Related papers  (2024-10-14T16:09:38Z)
• OpenFGL: A Comprehensive Benchmark for Federated Graph Learning [36.04858706246336]
  Federated graph learning (FGL) is a promising distributed training paradigm for graph neural networks across multiple local systems without direct data sharing.<n>Despite the proliferation of FGL, the diverse motivations from real-world applications, spanning various research backgrounds and settings, pose a significant challenge to fair evaluation.<n>We propose OpenFGL, a unified benchmark designed for the primary FGL scenarios: Graph-FL and Subgraph-FL.
  arXiv  Detail & Related papers  (2024-08-29T06:40:01Z)
• Noise-powered Multi-modal Knowledge Graph Representation Framework [52.95468915728721]
  The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph representation learning framework.<n>We propose a novel SNAG method that utilizes a Transformer-based architecture equipped with modality-level noise masking.<n>Our approach achieves SOTA performance across a total of ten datasets, demonstrating its versatility.
  arXiv  Detail & Related papers  (2024-03-11T15:48:43Z)
• FedGTA: Topology-aware Averaging for Federated Graph Learning [44.11777886421429]
  Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs. Most FGL optimization strategies ignore graph structure, presenting dissatisfied performance and slow convergence. We propose Federated Graph Topology-aware Aggregation (FedGTA), a personalized optimization strategy that optimize through topology-aware local smoothing confidence and mixed neighbor features.
  arXiv  Detail & Related papers  (2024-01-22T08:31:53Z)
• Multimodal Graph Learning for Generative Tasks [89.44810441463652]
  Multimodal learning combines multiple data modalities, broadening the types and complexity of data our models can utilize. We propose Multimodal Graph Learning (MMGL), a framework for capturing information from multiple multimodal neighbors with relational structures among them.
  arXiv  Detail & Related papers  (2023-10-11T13:25:03Z)
• FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning [65.48760613529033]
  Federated graph learning (FGL) has not been well supported due to its unique characteristics and requirements. We first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G) We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community.
  arXiv  Detail & Related papers  (2022-04-12T06:48:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.