4 citations · 6 across the 4 of their papers we have counts for
6 papers
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
Akira Imakura, Tetsuya Sakurai
Recently, federated learning has attracted much attention as a privacy-preserving integrated analysis that enables integrated analysis of data held by multiple institutions without…
New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis
Yuta Kawakami, Yuichi Takano, Akira Imakura
In recent years, the accumulation of data across various institutions has garnered attention for the technology of confidential data analysis, which improves analytical accuracy by…
Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings
Akihiro Mizoguchi, Anna Bogdanova, Akira Imakura +1
Given the time and expense associated with bringing a drug to market, numerous studies have been conducted to predict the properties of compounds based on their structure using mac…
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…
A filtering technique for the temporally reduced matrix of the Wilson fermion determinant
Yasunori Futamura, Shoji Hashimoto, Akira Imakura +2
The Wilson fermion determinant can be written in the form of a series expansion in fugacity , provided that the eigenmodes of the temporally reduced operator are obtai…