PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning

• URL: http://arxiv.org/abs/2606.09301v1
• Date: Mon, 08 Jun 2026 10:08:52 GMT
• Title: PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
• Abstract summary: 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.
• Score: 54.55726437469762
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may contain image--interaction graphs but no seller descriptions, while a catalog client may provide text but no product images. We refer to this practical setting as client-level modality deficiency. Unlike random instance-wise missingness, a deficient client lacks the local semantic basis needed to reconstruct the absent modality. More importantly, in graph learning, incomplete representations initialize message passing, so imputation errors can be filtered, mixed, and amplified by the receiving topology. To address this gap, we propose \textbf{PRISM} (\textbf{P}roactive \textbf{R}etrieval and \textbf{I}mputation via \textbf{S}tructural \textbf{M}eta-prompting), a topology-aware federated cross-modal imputation framework. Rather than reconstructing the missing modality solely from local observations, PRISM recovers missing-modality semantics from the federation and introduces them into local graph propagation under topology-aware control. Experiments on six multimodal graph datasets across graph-centric and modality-centric tasks show that PRISM consistently improves modality-deficient clients, outperforming state-of-the-art baselines by \textbf{4.48}\% on average.

Related papers

• Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning [63.602658976990874]
  Graph neural networks (GNNs) are typically conceptualized as message-passing neural networks.<n>We present a retrieval-augmented view of GNNs: each layer makes predictions by applying anvariant to a node representation together with a permutation-in summary of retrieved graph context.<n>Motivated by this perspective, we propose RTA, a simple aggregation-based framework that replaces structural message passing with label-aware retrieval and propagation.
  arXiv  Detail & Related papers  (2026-08-27T07:25:44Z)
• MMFGU: Multimodal Federated Graph Unlearning [26.774513573509108]
  We propose textsctextbfMMFGU, a multimodal graph unlearning framework built around target-specific representation decoupling.<n> Experiments show that textscMMFGU effectively removes requested information, preserves retained graph utility, and achieves a $boldsymbol41.5times$ speedup over full retraining.
  arXiv  Detail & Related papers  (2026-07-30T16:06:38Z)
• FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning [47.940785803361415]
  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.
  arXiv  Detail & Related papers  (2026-06-30T17:47:39Z)
• Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach [40.58607651570368]
  MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance.<n>We formalize modality-imbalanced MM-FGL as an implicit graph-aware latent semantic representation problem.<n>We propose FedMGS (Federated Modality-aware Graph Synthesis), which integrates three core components.
  arXiv  Detail & Related papers  (2026-06-18T15:40:55Z)
• STAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning [48.96526409527749]
  Federated graph learning (FGL) enables collaborative training on graph data across multiple clients.<n>The key challenge is that clients from different modality domains may not share a common semantic space.<n>We propose textbfSTAGE, a protocol-first framework for MM-FGL.
  arXiv  Detail & Related papers  (2026-05-12T10:35:43Z)
• Robust Multimodal Recommendation via Graph Retrieval-Enhanced Modality Completion [71.20734649881258]
  Real-world multimodal datasets often suffer from modality incompleteness due to sensor failures, annotation scarcity, or privacy constraints.<n>One effective solution is modality completion, which reconstructs missing features to provide modality-complete graphs for downstream tasks.<n>We propose GRE-MC, a Graph Retrieval-Enhanced Modality Completion framework, to overcome these limitations.
  arXiv  Detail & Related papers  (2026-05-01T13:50:52Z)
• Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss [26.31060859315329]
  We propose a novel textbfDynamic Deep textbfGraph Learning for textbfIncomplete textbfMulti-textbfView textbfView textbfClustering with textbfMasked Graph Reconstruction Loss (DGIMVCM)<n>A graph convolutional embedding layer is then designed to extract primary features and refined dynamic view-specific graph structures, leveraging the global graph for imputation of missing views.
  arXiv  Detail & Related papers  (2025-11-14T11:26:38Z)
• GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning [50.40400074353263]
  Graph Neural Networks (GNNs) are powerful tools for precessing relational data but often struggle to generalize to unseen graphs.<n>We introduce textbfGraph textbfIn-context textbfL textbfTransformer (GILT), a framework built on an LLM-free and tuning-free architecture.
  arXiv  Detail & Related papers  (2025-10-06T08:09:15Z)
• Beyond Message Passing: Neural Graph Pattern Machine [50.78679002846741]
  We introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures.<n>GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies.
  arXiv  Detail & Related papers  (2025-01-30T20:37:47Z)
• Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion [9.163655213173067]
  FedHGL is a first-of-its-kind framework for federated hypergraph learning on disjoint and privacy-constrained hypergraph partitions.<n>We develop FedHGL, a first-of-its-kind framework for federated hypergraph learning on disjoint and privacy-constrained hypergraph partitions.
  arXiv  Detail & Related papers  (2024-08-09T16:31:41Z)
• Redundancy-Free Self-Supervised Relational Learning for Graph Clustering [13.176413653235311]
  We propose a novel self-supervised deep graph clustering method named Redundancy-Free Graph Clustering (R$2$FGC) It extracts the attribute- and structure-level relational information from both global and local views based on an autoencoder and a graph autoencoder. Our experiments are performed on widely used benchmark datasets to validate the superiority of our R$2$FGC over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-09-09T06:18:50Z)
• Personalized Subgraph Federated Learning [56.52903162729729]
  We introduce a new subgraph FL problem, personalized subgraph FL, which focuses on the joint improvement of the interrelated local GNNs. We propose a novel framework, FEDerated Personalized sUBgraph learning (FED-PUB), to tackle it. We validate our FED-PUB for its subgraph FL performance on six datasets, considering both non-overlapping and overlapping subgraphs.
  arXiv  Detail & Related papers  (2022-06-21T09:02:53Z)
• Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning [21.0019144298605]
  Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. textscSubg-Con is proposed by utilizing the strong correlation between central nodes and their sampled subgraphs to capture regional structure information. Compared with existing graph representation learning approaches, textscSubg-Con has prominent performance advantages in weaker supervision requirements, model learning scalability, and parallelization.
  arXiv  Detail & Related papers  (2020-09-22T01:58:19Z)
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification [84.43394420267794]
  We propose a novel framework by learning high-order relation and topology information for discriminative features and robust alignment. Our framework significantly outperforms state-of-the-art by6.5%mAP scores on Occluded-Duke dataset.
  arXiv  Detail & Related papers  (2020-03-18T12:18:35Z)

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.