RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
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FedMGS formalizes modality-imbalanced MM-FGL as latent semantic synthesis and uses availability-aware encoding, prototype-guided synthesis, and reliability-calibrated fusion to recover missing modalities, reporting up to 17.41% gains on four tasks.
Real-world testbed experiments demonstrate that protocol-compliant UDP flooding and oversized BSM attacks on C-V2X can reduce packet delivery ratios by up to 87%, increase latency beyond 400 ms, and completely suppress FCW alerts.
citing papers explorer
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RoleMAG: Learning Neighbor Roles in Multimodal Graphs
RoleMAG learns neighbor roles in multimodal graphs to route shared, complementary, and heterophilous signals through separate channels, improving propagation without modality interference.
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Towards Modality-imbalanced Federated Graph Learning: A Data Synthesis-based Approach
FedMGS formalizes modality-imbalanced MM-FGL as latent semantic synthesis and uses availability-aware encoding, prototype-guided synthesis, and reliability-calibrated fusion to recover missing modalities, reporting up to 17.41% gains on four tasks.
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Real-World Evaluation of Protocol-Compliant Denial-of-Service Attacks on C-V2X-based Forward Collision Warning Systems
Real-world testbed experiments demonstrate that protocol-compliant UDP flooding and oversized BSM attacks on C-V2X can reduce packet delivery ratios by up to 87%, increase latency beyond 400 ms, and completely suppress FCW alerts.