Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach

• URL: http://arxiv.org/abs/2606.20382v1
• Date: Thu, 18 Jun 2026 15:40:55 GMT
• Title: Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
• Abstract summary: 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.
• Score: 40.58607651570368
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MultiModal Federated Graph Learning (MM-FGL) offers a natural collaborative training paradigm, but its practical deployment is challenged by two granularities of modality imbalance. Client-level imbalance occurs when certain clients lack entire modalities, while node-level imbalance occurs when individual nodes exhibit missing visual or textual attributes. While several relevant studies exist, our investigation reveals that they predominantly target graph-agnostic or centralized scenarios, rendering them difficult to adapt directly. To address these challenges, we formalize modality-imbalanced MM-FGL as an implicit graph-aware latent semantic representation synthesis problem. This paradigm recovers missing modal semantics directly within the representation space, thereby maximizing alignment with the original data's semantic distribution and mitigating the high variance induced by missing modalities. To this end, we propose FedMGS (Federated Modality-aware Graph Synthesis), which integrates three core components. The availability-aware graph encoder prevents missing modalities from contaminating local structural propagation. The prototype-guided latent semantic synthesizer establishes cross-client semantic anchors for unavailable modalities. The reliability-calibrated semantic fusion mechanism regulates the impact of recovered latent representations prior to predictive readout. Extensive experiments on four tasks show that FedMGS consistently outperforms competitive baselines with gains up to 17.41% with best efficiency-performance tradeoff.

Related papers

• 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)
• Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs [77.42033827176806]
  One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm.<n>We propose a novel Federated Model Inversion and Token Relabel framework, which trains the global model by fully exploiting all patches of synthetic images.
  arXiv  Detail & Related papers  (2026-05-11T15:49:08Z)
• 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)
• OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows [9.617220633655716]
  We present textbfunderlineOmni-textbfunderlineModality textbfunderlineGeneration Agent (textbfOMG-Agent)
  arXiv  Detail & Related papers  (2026-02-04T02:25:40Z)
• Rethinking Federated Graph Foundation Models: A Graph-Language Alignment-based Approach [8.517604507672262]
  Recent studies of federated graph foundational models (FedGFMs) break the idealized and untenable assumption of having centralized data storage to train graph foundation models.<n>Existing studies that project aligned generalizable knowledge onto a discrete token space via vector-quantized backbones suffer from irreversible knowledge loss during the quantization process.
  arXiv  Detail & Related papers  (2026-01-29T07:50:00Z)
• Beyond Simple Fusion: Adaptive Gated Fusion for Robust Multimodal Sentiment Analysis [27.11612547025828]
  We introduce textbfAdaptive textbfGated textbfFusion textbfNetwork that adaptively adjusts feature weights based on information entropy and modality importance.<n>Experiments on CMU-MOSI and CMU-MOSEI show that AGFN significantly outperforms strong baselines in accuracy, effectively discerning subtle emotions with robust performance.
  arXiv  Detail & Related papers  (2025-10-02T05:05:41Z)
• MIDAS: Misalignment-based Data Augmentation Strategy for Imbalanced Multimodal Learning [14.06705718861471]
  Multimodal models often over-rely on dominant modalities, failing to achieve optimal performance.<n>We propose MIDAS, a novel data augmentation strategy that generates misaligned samples with semantically inconsistent cross-modal information.<n>Experiments on multiple multimodal classification benchmarks demonstrate that MIDAS significantly outperforms related baselines in addressing modality imbalance.
  arXiv  Detail & Related papers  (2025-09-30T06:13:17Z)
• Reducing Unimodal Bias in Multi-Modal Semantic Segmentation with Multi-Scale Functional Entropy Regularization [66.10528870853324]
  Fusing and balancing multi-modal inputs from novel sensors for dense prediction tasks is critically important.<n>One major limitation is the tendency of multi-modal frameworks to over-rely on easily learnable modalities.<n>We propose a plug-and-play regularization term based on functional entropy, which introduces no additional parameters.
  arXiv  Detail & Related papers  (2025-05-10T12:58:15Z)
• Balancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition [3.330229314824913]
  Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks.<n>We show that existing methods position opposite ends of a spectrum driven by the graph embedding smoothness, with each end corresponding to outperformance on specific downstream tasks.<n>A framework, BSG, introduces novel loss functions designed to supplement the representation quality in graph-based SSL.
  arXiv  Detail & Related papers  (2025-04-16T12:09:56Z)
• Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer [72.5190560787569]
  In computer vision, learning from long tailed datasets is a recurring theme, especially for natural image datasets. Our proposal posits a meta-distributional scenario, where the data generating mechanism is invariant across the label-conditional feature distributions. This allows us to leverage a causal data inflation procedure to enlarge the representation of minority classes.
  arXiv  Detail & Related papers  (2020-11-25T00:13:11Z)

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.