1 citations · 1 across the 10 of their papers we have counts for
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Geometric Data Perturbation with Noisy-Anchor Alignment for Privacy-Preserving Collaborative Learning
Keiyu Nosaka, Yamato Suetake, Yuichi Takano +2
Geometric data perturbation enables one-shot representation sharing for privacy-preserving collaborative learning: each participant applies a secret distance-preserving transformat…
Covariance-Based Structural Equation Modeling in Small-Sample Settings with
Hiroki Hasegawa, Aoba Tamura, Yukihiko Okada
Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings wi…
Interaction Tensor SHAP
Hiroki Hasegawa, Yukihiko Okada
This study proposes Interaction Tensor SHAP (IT-SHAP), a tensor algebraic formulation of the Shapley Taylor Interaction Index (STII) that makes its computational structure explicit…
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…
A Robust and Non-Iterative Tensor Decomposition Method with Automatic Thresholding
Hiroki Hasegawa, Yukihiko Okada
Recent advances in IoT and biometric sensing technologies have led to the generation of massive and high-dimensional tensor data, yet achieving accurate and efficient low-rank appr…
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…