3 papers
cs.LG2026
Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric Data Perturbation (GDP) enables one-shot, privacy-preserving collaborative learning: each participant applies a distance-preserving transformation to its private data and…
cs.LG2026
Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis
Yamato Suetake, Yuta Kawakami, Shunnosuke Ikeda +1
Collaborative analysis of decentralized confidential datasets is important, but direct sharing of original datasets is often restricted by privacy and institutional constraints. Da…
cs.LG2026
Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +1
Data Collaboration (DC) enables multiple parties to jointly train a model by sharing only linear projections of their private datasets. The core challenge in DC is to align the bas…