Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretable collaborative data analysis on distributed data   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.

4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom