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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…
stat.ME2024
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…