13 citations · 31 across the 7 of their papers we have counts for
4 papers · 1 filter
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
Akira Imakura, Tetsuya Sakurai, Yukihiko Okada +3
Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has considerable research attention. For the datasets of multiple medi…
Another Use of SMOTE for Interpretable Data Collaboration Analysis
Akira Imakura, Masateru Kihira, Yukihiko Okada +1
Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually construc…
Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations
Anna Bogdanova, Akie Nakai, Yukihiko Okada +2
Dimensionality Reduction is a commonly used element in a machine learning pipeline that helps to extract important features from high-dimensional data. In this work, we explore an…
Interpretable collaborative data analysis on distributed data
Akira Imakura, Hiroaki Inaba, Yukihiko Okada +1
This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distr…