Data collaboration analysis for distributed datasets
This addresses privacy-preserving data analysis for institutions with large, distributed datasets, though it appears incremental as it builds on centralized machine learning approaches.
The paper tackles the challenge of analyzing distributed datasets without centralizing raw data due to size or privacy concerns by proposing a method that centralizes only intermediate representations, achieving higher prediction performance compared to individual analyses.
In this paper, we propose a data collaboration analysis method for distributed datasets. The proposed method is a centralized machine learning while training datasets and models remain distributed over some institutions. Recently, data became large and distributed with decreasing costs of data collection. If we can centralize these distributed datasets and analyse them as one dataset, we expect to obtain novel insight and achieve a higher prediction performance compared with individual analyses on each distributed dataset. However, it is generally difficult to centralize the original datasets due to their huge data size or regarding a privacy-preserving problem. To avoid these difficulties, we propose a data collaboration analysis method for distributed datasets without sharing the original datasets. The proposed method centralizes only intermediate representation constructed individually instead of the original dataset.