  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.
