Abstract

This paper presents a privacy-preserving framework for distributed neural network modeling across heterogeneous data sources, where local datasets differ in both objects and attributes. To enable collaborative learning without sharing raw data or model parameters, each local decision table is independently transformed into a unified feature space using multiple dimensionality reduction techniques – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Uniform Manifold Approximation and Projection (UMAP). Various types of neural networks – Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Simple Recurrent Network (SIMPLE), Multilayer Perceptron (MLP) and the Radial Basis Function Network (RBF) – are trained locally, and their outputs are aggregated using soft voting (simple average) to generate final predictions. Experimental results on benchmark datasets confirm the approach’s effectiveness, scalability, and robustness in decentralized learning settings.

Recommended Citation

Marfo, K.F. & Przybyła-Kasperek, M. (2025). Decentralized Neural Network Modeling from Heterogeneous Data Sources: A Feature Mapping ApproachIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.39

Paper Type

Short Paper

DOI

10.62036/ISD.2025.39

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Decentralized Neural Network Modeling from Heterogeneous Data Sources: A Feature Mapping Approach

This paper presents a privacy-preserving framework for distributed neural network modeling across heterogeneous data sources, where local datasets differ in both objects and attributes. To enable collaborative learning without sharing raw data or model parameters, each local decision table is independently transformed into a unified feature space using multiple dimensionality reduction techniques – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Uniform Manifold Approximation and Projection (UMAP). Various types of neural networks – Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Simple Recurrent Network (SIMPLE), Multilayer Perceptron (MLP) and the Radial Basis Function Network (RBF) – are trained locally, and their outputs are aggregated using soft voting (simple average) to generate final predictions. Experimental results on benchmark datasets confirm the approach’s effectiveness, scalability, and robustness in decentralized learning settings.