DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning

Anna Bogdanova, Akira Imakura, Tetsuya Sakurai

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DOI: 10.1007/s44230-023-00032-4
Published: 2023-07-06
Container: Human-Centric Intelligent Systems
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s44230-023-00032-4,
  title = {DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning},
  author = {Anna Bogdanova and Akira Imakura and Tetsuya Sakurai},
  year = {2023},
  journal = {Human-Centric Intelligent Systems},
  doi = {10.1007/s44230-023-00032-4},
  url = {https://doi.org/10.1007/s44230-023-00032-4}
}

RIS

TY  - JOUR
TI  - DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning
AU  - Anna Bogdanova
AU  - Akira Imakura
AU  - Tetsuya Sakurai
PY  - 2023
JO  - Human-Centric Intelligent Systems
DO  - 10.1007/s44230-023-00032-4
UR  - https://doi.org/10.1007/s44230-023-00032-4
ER  -

APA

Bogdanova, A., Imakura, A., & Sakurai, T. (2023). DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning. Human-Centric Intelligent Systems. https://doi.org/10.1007/s44230-023-00032-4

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crossref · retrieved 2026-11-10T13:00:05.317Z