1 citations · 1 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…