4 papers
Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data
Sota Sugawara, Yuji Kawamata, Akihiro Toyoda +2
Federated Learning (FL) enables distributed learning across multiple clients without sharing raw data. When statistical heterogeneity across clients is severe, Clustered Federated…
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…
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…