Hirozumi Yamaguchi
1 indexed paper
Recent (6 mo)
1With code
0Influential cites
0Benchmarked
0Publications per year
126
Top categories
ML×1AI×1Crypto×1Distributed×1
Frequent co-authors
Research Timeline
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.
Highlighted terms show continued research focus across papers
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…
View →