论文标题
分布式数据的可解释的协作数据分析
Interpretable collaborative data analysis on distributed data
论文作者
论文摘要
本文提出了一种可解释的非模型共享协作数据分析方法作为联合学习系统之一，这是一种用于分析分布式数据的新兴技术。在许多应用程序，例如由于隐私和机密性问题而进行的医疗，财务和制造数据分析等许多应用中，分析数据分析至关重要。此外，获得的模型的可解释性对于联合学习系统的实际应用具有重要作用。通过集中在各方中单独构建的中间表示，提出的方法获得了可解释的模型，实现了协作分析，而无需揭示分布在本地各方的单个数据和学习模型。数值实验表明，所提出的方法比个人分析获得了人工和现实世界中的问题更好的识别性能。
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.
