PWU Journal of Research, Innovation, and Transformation
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TY - JOUR AU - Jr, Daniel Dasig AU - Dhar, Bablu Kumar AU - Pascua, Sonia M. AU - Austria, Milani PY - 2026 DA - 2026/03/03 TI - Federated Neuro-Symbolic Intelligence for Privacy-Preserving Data Analytics: A Next-Generation Framework for Real-Time and Industry Applications JO - PWU Journal of Research, Innovation, and Transformation T2 - PWU Journal of Research, Innovation, and Transformation JF - PWU Journal of Research, Innovation, and Transformation VL - 1 IS - 1 SP - 28 EP - 38 DO - 10.62762/JRIT.2026.408887 UR - https://www.icck.org/article/abs/JRIT.2026.408887 KW - federated learning KW - neuro-symbolic artificial intelligence KW - privacy-preserving analytics KW - explainable AI KW - distributed intelligence KW - real-time systems AB - The growth of distributed, institutionally siloed data has created demand for analytics frameworks that ensure privacy, interpretability, and real-time decision support. While federated learning enables decentralized model training without raw data sharing, most existing approaches rely on opaque neural models and lack explicit reasoning capabilities. This paper proposes a Federated Neuro-Symbolic Intelligence (FNSI) framework that integrates federated learning with symbolic reasoning to address these limitations. The architecture combines local neural learning with symbolic constraint enforcement at the client level and a privacy-preserving coordination layer that aggregates encrypted updates and harmonizes distributed knowledge. Formal algorithms and theoretical analysis establish convergence, logical soundness, and data non-mobility. Experimental results under heterogeneous and non-IID data demonstrate that FNSI achieves accuracy comparable to centralized learning, improved robustness over standard federated averaging, and a substantial reduction in logical constraint violations. Visualization-based analyses further show how symbolic projection corrects infeasible neural outputs while preserving predictive fidelity. Overall, FNSI provides a balanced solution for trustworthy, cross-industry analytics in regulated environments. SN - pending PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Jr2026Federated,
author = {Daniel Dasig Jr and Bablu Kumar Dhar and Sonia M. Pascua and Milani Austria},
title = {Federated Neuro-Symbolic Intelligence for Privacy-Preserving Data Analytics: A Next-Generation Framework for Real-Time and Industry Applications},
journal = {PWU Journal of Research, Innovation, and Transformation},
year = {2026},
volume = {1},
number = {1},
pages = {28-38},
doi = {10.62762/JRIT.2026.408887},
url = {https://www.icck.org/article/abs/JRIT.2026.408887},
abstract = {The growth of distributed, institutionally siloed data has created demand for analytics frameworks that ensure privacy, interpretability, and real-time decision support. While federated learning enables decentralized model training without raw data sharing, most existing approaches rely on opaque neural models and lack explicit reasoning capabilities. This paper proposes a Federated Neuro-Symbolic Intelligence (FNSI) framework that integrates federated learning with symbolic reasoning to address these limitations. The architecture combines local neural learning with symbolic constraint enforcement at the client level and a privacy-preserving coordination layer that aggregates encrypted updates and harmonizes distributed knowledge. Formal algorithms and theoretical analysis establish convergence, logical soundness, and data non-mobility. Experimental results under heterogeneous and non-IID data demonstrate that FNSI achieves accuracy comparable to centralized learning, improved robustness over standard federated averaging, and a substantial reduction in logical constraint violations. Visualization-based analyses further show how symbolic projection corrects infeasible neural outputs while preserving predictive fidelity. Overall, FNSI provides a balanced solution for trustworthy, cross-industry analytics in regulated environments.},
keywords = {federated learning, neuro-symbolic artificial intelligence, privacy-preserving analytics, explainable AI, distributed intelligence, real-time systems},
issn = {pending},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
PWU Journal of Research, Innovation, and Transformation
ISSN: pending (Online)
Email: [email protected]
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