Shang-Tse Chen
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126
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ML×1AI×1Crypto×1Distributed×1
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2026
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
UMEDA introduces a novel graph federated learning framework that uses spectral signal processing and diffusion models to enable privacy-preserving, robust localization across clients with highly heterogeneous sensor modalities and data distributions.
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Papers
cs.LGcs.AIcs.CRRecentMay 8, 2026
UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment
Shih-Yu Lai, Hirozumi Yamaguchi, Shang-Tse Chen, Yu-Lun Liu +1 more
UMEDA introduces a novel graph federated learning framework that uses spectral signal processing and diffusion models to enable privacy-preserving, robust localization across clients with highly heter…
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