Interpretable collaborative data analysis on distributed data

TL;DR

This paper introduces an interpretable federated learning method that enables collaborative analysis of distributed data without compromising privacy, emphasizing interpretability and improved recognition performance.

Contribution

It presents a novel non-model sharing federated analysis approach that centralizes intermediate representations for interpretability and enhanced accuracy.

Findings

Achieves better recognition performance than individual analysis.

Maintains data privacy by not revealing local data or models.

Provides an interpretable analysis framework for distributed data.

Abstract

This paper proposes an interpretable non-model sharing collaborative data analysis method as one of the federated learning systems, which is an emerging technology to analyze distributed data. Analyzing distributed data is essential in many applications such as medical, financial, and manufacturing data analyses due to privacy, and confidentiality concerns. In addition, interpretability of the obtained model has an important role for practical applications of the federated learning systems. By centralizing intermediate representations, which are individually constructed in each party, the proposed method obtains an interpretable model, achieving a collaborative analysis without revealing the individual data and learning model distributed over local parties. Numerical experiments indicate that the proposed method achieves better recognition performance for artificial and real-world problems than individual analysis.