Interpretable collaborative data analysis on distributed data

作者:

Highlights:

• An interpretable distributed data analysis with sharing intermediate representations.

• A practical supplement to the federated learning systems.

• The obtained interpretable model is based on the whole features of distributed data.

• Each party can individually select an interpretable model according to its own needs.

• The proposed method achieves good recognitions for artificial and real-world data.

摘要

•An interpretable distributed data analysis with sharing intermediate representations.•A practical supplement to the federated learning systems.•The obtained interpretable model is based on the whole features of distributed data.•Each party can individually select an interpretable model according to its own needs.•The proposed method achieves good recognitions for artificial and real-world data.

论文关键词:Collaborative data analysis,Distributed data,Interpretability,Dimensionality reduction

论文评审过程:Received 26 June 2020, Revised 25 December 2020, Accepted 7 March 2021, Available online 17 March 2021, Version of Record 10 April 2021.

论文官网地址:https://doi.org/10.1016/j.eswa.2021.114891