FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

• URL: http://arxiv.org/abs/2606.32016v1
• Date: Tue, 30 Jun 2026 17:47:39 GMT
• Title: FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
• Abstract summary: FedLAB is a traceable semantic codebook framework that organizes multimodal graph knowledge into typed codebooks for modality evidence, node semantics, and topology context.<n>Experiments show that FedLAB improves over state-of-the-art baselines by up to 7.53%, while preserving a native semantic trace interface.
• Score: 47.940785803361415
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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. In practice, however, such multimodal graphs are often distributed across decentralized clients, where raw contents and local structures cannot be centrally shared due to privacy constraints. This motivates federated multimodal graph foundation learning, which requires not only transferable representation learning but also intrinsic semantic traceability under strict data isolation. Existing methods usually exchange or store knowledge through parameters, prototypes, embeddings, or compact codebooks, which support optimization and transfer but do not explicitly expose how modality evidence, node semantics, and topology context jointly support predictions. To bridge this gap, we propose FedLAB, a traceable semantic codebook framework that organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context. FedLAB further refines these trace units through federated semantic barycenter pre-training while keeping raw multimodal contents and graph structures local. Extensive experiments on 10 benchmarks and 6 downstream tasks show that FedLAB improves over state-of-the-art baselines by up to 7.53\%, while preserving a native semantic trace interface.

Related papers

• Toward Federated Multimodal Graph Foundation Models: A Topology-Aware Multimodal Alignment Framework [35.35423006939282]
  FedGAMMA casts federated multimodal graph foundation learning as a two-stage semantic-structural alignment problem.<n>During pre-training, a shared-private semantic enhancer disentangles cross-modal commonality from modality-specific information.<n>During fine-tuning, FedGAMMA adapts the pretrained encoder through lightweight graph-aware prompts.
  arXiv  Detail & Related papers  (2026-07-17T06:57:41Z)
• PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning [54.55726437469762]
  We propose textbfPRISM (textbfProactive textbfRetrieval and textbfImputation via textbfStructural textbfMeta-prompting), a topology-aware cross-modal imputation framework.<n>Experiments show PRISM consistently improves modality-deficient clients, outperforming state-of-the-art baselines by textbf4.48% on average.
  arXiv  Detail & Related papers  (2026-06-08T10:08:52Z)
• MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image [52.47434184153733]
  We introduce MulTaBench, a benchmark of 40 datasets split equally between image-tabular and text-tabular tasks.<n>We show that the gains from target-aware representation tuning generalize across both text and image modalities.
  arXiv  Detail & Related papers  (2026-05-11T14:12:05Z)
• OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection [48.77471686671269]
  OWLEYE is a novel framework that learns transferable patterns of normal behavior from multiple graphs.<n>We show that OWLEYE achieves superior performance and generalizability compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-01-27T02:08:18Z)
• MetaHGNIE: Meta-Path Induced Hypergraph Contrastive Learning in Heterogeneous Knowledge Graphs [16.36978652807043]
  We propose MetaHGNIE, a meta-path induced hypergraph contrastive learning framework for disentangling and aligning structural and semantic information.<n>Our results highlight the effectiveness of explicitly modeling higher-order interactions and cross-modal alignment in heterogeneous knowledge graphs.
  arXiv  Detail & Related papers  (2025-12-13T22:21:33Z)
• Quantizing Text-attributed Graphs for Semantic-Structural Integration [6.721504414917793]
  Text-attributed graphs (TAGs) have emerged as a powerful representation for modeling complex relationships across diverse domains.<n>With the rise of large language models (LLMs), there is growing interest in leveraging their capabilities for graph learning.<n>We propose STAG, a novel self-supervised framework that directly quantizes graph structural information into discrete tokens using a frozen codebook.
  arXiv  Detail & Related papers  (2025-07-20T09:18:02Z)
• Leveraging Foundation Models for Multimodal Graph-Based Action Recognition [2.066890710233268]
  We introduce a graph-based framework that integrates a vision-temporal foundation leveraging VideoMAE for dynamic visual encoding and BERT for contextual textual embedding.<n>We show that our method consistently outperforms state-of-the-art baselines on diverse benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-21T07:15:14Z)
• 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)
• KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification [75.95647590619929]
  Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. We propose a Knowledge-Aware Multi-Faceted framework (KMF) that enhances the richness of label semantics. A novel geometric constraint is developed to alleviate the problem of prototype drift caused by node information aggregation.
  arXiv  Detail & Related papers  (2023-08-15T02:38:08Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

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.