Achieving Transparency in Distributed Machine Learning with Explainable Data Collaboration

no code implementations • 6 Dec 2022 • Anna Bogdanova, Akira Imakura, Tetsuya Sakurai, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use.

Privacy Preserving

Non-readily identifiable data collaboration analysis for multiple datasets including personal information

no code implementations • 31 Aug 2022 • Akira Imakura, Tetsuya Sakurai, Yukihiko Okada, Tomoya Fujii, Teppei Sakamoto, Hiroyuki Abe

This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information.