# Another Use of SMOTE for Interpretable Data Collaboration Analysis

arXiv: https://arxiv.org/abs/2208.12458 · HTML: https://mlsanity.com/paper/2208.12458/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Interpretable collaborative data analysis on distributed data](https://mlsanity.com/paper/2011.04437/) (2020)

## Built on by (most cited first, 10 of 10)

- [Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach](https://mlsanity.com/paper/2501.12723/) (2025)
- [New Solutions Based on the Generalized Eigenvalue Problem for the Data Collaboration Analysis](https://mlsanity.com/paper/2404.14164/) (2024)
- [Data Collaboration Analysis with Orthonormal Basis Selection and Alignment](https://mlsanity.com/paper/2403.02780/) (2024)
- [Privacy-preserving recommender system using the data collaboration analysis for distributed datasets](https://mlsanity.com/paper/2406.01603/) (2024)
- [FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration](https://mlsanity.com/paper/2409.18356/) (2024)
- [Data Collaboration Analysis applied to Compound Datasets and the Introduction of Projection data to Non-IID settings](https://mlsanity.com/paper/2308.00280/) (2023)
- [A new type of federated clustering: A non-model-sharing approach](https://mlsanity.com/paper/2506.10244/) (2025)
- [Single-Round Clustered Federated Learning via Data Collaboration Analysis for Non-IID Data](https://mlsanity.com/paper/2601.09304/) (2026)
- [Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis](https://mlsanity.com/paper/2605.27219/) (2026)
- [DNN-GDITD: Out-of-distribution detection via Deep Neural Network based Gaussian Descriptor for Imbalanced Tabular Data](https://mlsanity.com/paper/2409.00980/) (2024)

## Abstract (arXiv)

Recently, data collaboration (DC) analysis has been developed for privacy-preserving integrated analysis across multiple institutions. DC analysis centralizes individually constructed dimensionality-reduced intermediate representations and realizes integrated analysis via collaboration representations without sharing the original data. To construct the collaboration representations, each institution generates and shares a shareable anchor dataset and centralizes its intermediate representation. Although, random anchor dataset functions well for DC analysis in general, using an anchor dataset whose distribution is close to that of the raw dataset is expected to improve the recognition performance, particularly for the interpretable DC analysis. Based on an extension of the synthetic minority over-sampling technique (SMOTE), this study proposes an anchor data construction technique to improve the recognition performance without increasing the risk of data leakage. Numerical results demonstrate the efficiency of the proposed SMOTE-based method over the existing anchor data constructions for artificial and real-world datasets. Specifically, the proposed method achieves 9 percentage point and 38 percentage point performance improvements regarding accuracy and essential feature selection, respectively, over existing methods for an income dataset. The proposed method provides another use of SMOTE not for imbalanced data classifications but for a key technology of privacy-preserving integrated analysis.