Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion

• URL: http://arxiv.org/abs/2506.22036v1
• Date: Fri, 27 Jun 2025 09:32:58 GMT
• Title: Hyper-modal Imputation Diffusion Embedding with Dual-Distillation for Federated Multimodal Knowledge Graph Completion
• Authors: Ying Zhang, Yu Zhao, Xuhui Sui, Baohang Zhou, Xiangrui Cai, Li Shen, Xiaojie Yuan, Dacheng Tao,
• Abstract summary: We propose a framework named MMFeD3-HidE for addressing multimodal uncertain unavailability and multimodal client heterogeneity challenges of FedMKGC.<n>We propose a FedMKGC benchmark for a comprehensive evaluation, consisting of a general FedMKGC backbone named MMFedE, datasets with heterogeneous multimodal information, and three groups of constructed baselines.
• Score: 59.54067771781552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing multimodal knowledge privatization requirements, multimodal knowledge graphs in different institutes are usually decentralized, lacking of effective collaboration system with both stronger reasoning ability and transmission safety guarantees. In this paper, we propose the Federated Multimodal Knowledge Graph Completion (FedMKGC) task, aiming at training over federated MKGs for better predicting the missing links in clients without sharing sensitive knowledge. We propose a framework named MMFeD3-HidE for addressing multimodal uncertain unavailability and multimodal client heterogeneity challenges of FedMKGC. (1) Inside the clients, our proposed Hyper-modal Imputation Diffusion Embedding model (HidE) recovers the complete multimodal distributions from incomplete entity embeddings constrained by available modalities. (2) Among clients, our proposed Multimodal FeDerated Dual Distillation (MMFeD3) transfers knowledge mutually between clients and the server with logit and feature distillation to improve both global convergence and semantic consistency. We propose a FedMKGC benchmark for a comprehensive evaluation, consisting of a general FedMKGC backbone named MMFedE, datasets with heterogeneous multimodal information, and three groups of constructed baselines. Experiments conducted on our benchmark validate the effectiveness, semantic consistency, and convergence robustness of MMFeD3-HidE.

Related papers

• BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation [55.486872677160015]
  We reformulate multi-modal semantic segmentation as a mask-level classification task.<n>We propose BiXFormer, which integrates Unified Modality Matching (UMM) and Cross Modality Alignment (CMA)<n> Experiments on both synthetic and real-world multi-modal benchmarks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2025-06-04T08:04:58Z)
• CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG [53.950029990391066]
  Cross-source knowledge textbfReconciliation for Multimodal RAG (CoRe-MMRAG)<n>We propose a novel end-to-end framework that effectively reconciles inconsistencies across knowledge sources.<n>Experiments on KB-VQA benchmarks show that CoRe-MMRAG achieves substantial improvements over baseline methods.
  arXiv  Detail & Related papers  (2025-06-03T07:32:40Z)
• A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network [53.37983409425452]
  Large Language Models (LLMs) have achieved remarkable success across a wide range of applications.<n>Recently, collaborative frameworks such as the Multi-LLM Network (MultiLLMN) have been introduced.<n>We propose a novel Trusted MultiLLMN framework driven by a weighted Byzantine Fault Tolerance (WBFT) blockchain consensus mechanism.
  arXiv  Detail & Related papers  (2025-05-08T10:04:41Z)
• Sparsely Multimodal Data Fusion [0.0]
  This paper presents a comparative study of three multimodal embedding techniques, Modal Channel Attention (MCA), Zorro, and Everything at Once (EAO)<n>MCA introduces fusion embeddings for all combinations of input modalities and uses attention masking to create distinct attention channels.<n>MCA achieves superior performance by maintaining robust uniformity across unimodal and fusion embeddings.
  arXiv  Detail & Related papers  (2024-03-29T16:49:40Z)
• FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology [3.802258033231335]
  Federated Multi-Modal (FedMM) is a learning framework that trains multiple single-modal feature extractors to enhance subsequent classification performance. FedMM notably outperforms two baselines in accuracy and AUC metrics.
  arXiv  Detail & Related papers  (2024-02-24T16:58:42Z)
• Examining Modality Incongruity in Multimodal Federated Learning for Medical Vision and Language-based Disease Detection [7.515840210206994]
  The impact of missing modality in different clients, also called modality incongruity, has been greatly overlooked. This paper, for the first time, analyses the impact of modality incongruity and reveals its connection with data heterogeneity across participating clients.
  arXiv  Detail & Related papers  (2024-02-07T22:16:53Z)
• Provable Dynamic Fusion for Low-Quality Multimodal Data [94.39538027450948]
  Dynamic multimodal fusion emerges as a promising learning paradigm. Despite its widespread use, theoretical justifications in this field are still notably lacking. This paper provides theoretical understandings to answer this question under a most popular multimodal fusion framework from the generalization perspective. A novel multimodal fusion framework termed Quality-aware Multimodal Fusion (QMF) is proposed, which can improve the performance in terms of classification accuracy and model robustness.
  arXiv  Detail & Related papers  (2023-06-03T08:32:35Z)
• Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation [16.308470947384134]
  HA-Fedformer is a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client. We develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models.
  arXiv  Detail & Related papers  (2023-03-27T07:07:33Z)
• Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis [96.46952672172021]
  Bi-Bimodal Fusion Network (BBFN) is a novel end-to-end network that performs fusion on pairwise modality representations. Model takes two bimodal pairs as input due to known information imbalance among modalities.
  arXiv  Detail & Related papers  (2021-07-28T23:33:42Z)

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.