Interpretable collaborative data analysis on 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.

16 pages, 3 figures, 3 tables

References in corpus (6)

• Deep Learning with Differential Privacy
• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Differential Privacy and Machine Learning: a Survey and Review
• Stakeholders in Explainable AI
• Federated Learning System without Model Sharing through Integration of Dimensional Reduced Data Representations