FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
Openfgl: A comprehensive benchmark for federated graph learning.arXiv preprint arXiv:2408.16288, 2024
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PRISM proposes a topology-aware cross-modal imputation framework for client-level modality-deficient multimodal federated graph learning that improves deficient clients by 4.48% on average over baselines across six datasets.
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.
citing papers explorer
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FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning
FedLAB organizes multimodal graph knowledge into typed hierarchical codebooks for modality evidence, node semantics, and topology context via federated semantic barycenter pre-training, improving performance by up to 7.53% on benchmarks while enabling semantic traceability.
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PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
PRISM proposes a topology-aware cross-modal imputation framework for client-level modality-deficient multimodal federated graph learning that improves deficient clients by 4.48% on average over baselines across six datasets.
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UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA is a new graph federated learning method that uses low-rank spectral filtering and diffusion over a shared integral operator to fuse multi-modal data privately, outperforming baselines on MM-Fi and RELI11D under high heterogeneity and tight privacy budgets.