13 citations · 22 across the 11 of their papers we have counts for
11 papers
A new type of federated clustering: A non-model-sharing approach
Yuji Kawamata, Kaoru Kamijo, Masateru Kihira +5
In recent years, the growing need to leverage sensitive data across institutions has led to increased attention on federated learning (FL), a decentralized machine learning paradig…
Estimating Covariate-balanced Survival Curve in Distributed Data Environment using Data Collaboration Quasi-Experiment
Akihiro Toyoda, Yuji Kawamata, Tomoru Nakayama +3
The sharing of patient-level data necessary for covariate-adjusted survival analysis between medical institutions is difficult due to privacy protection restrictions. We propose a…
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach
Sota Mashiko, Yuji Kawamata, Tomoru Nakayama +2
Anomaly detection is crucial in financial auditing, and effective detection requires large volumes of data from multiple organizations. However, journal entry data is highly sensit…
Data collaboration for causal inference from limited medical testing and medication data
Tomoru Nakayama, Yuji Kawamata, Akihiro Toyoda +7
Observational studies enable causal inferences when randomized controlled trials (RCTs) are not feasible. However, integrating sensitive medical data across multiple institutions i…
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…
Estimation of conditional average treatment effects on distributed confidential data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada +2
The estimation of conditional average treatment effects (CATEs) is an important topic in many scientific fields. CATEs can be estimated with high accuracy if data distributed acros…
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…
Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration
Anna Bogdanova, Akira Imakura, Tetsuya Sakurai +3
Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use. To that end, feature attribution methods s…
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…
Collaborative causal inference on distributed data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada +2
In recent years, the development of technologies for causal inference with privacy preservation of distributed data has gained considerable attention. Many existing methods for dis…