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.
Metafi++: Wifi-enabled transformer-based human pose estimation for metaverse avatar simulation.IEEE Internet of Things Journal, 10(16):14128–14136
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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.