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cs.LG2026
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…
cs.LG2024
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…
cs.LG2024
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…