Trustworthy and explainable federated system for extracting descriptive rules in a data streaming environment

• María Asunción Padilla-Rascón ^[1] ; Ángel Miguel García Vico ; Cristóbal José Carmona del Jesús
  1. [1] Universidad de Jaén

     Universidad de Jaén

     Jaén, España

     
• Localización: Actas del XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados: (MAEB 2025) 28-30 de mayo, Donostia/San Sebastián / coord. por Leticia Hernando Rodríguez , Josu Ceberio Uribe , Jon Vadillo Jueguen, 2025, ISBN 978-84-1319-656-5, págs. 343-346
• Idioma: inglés
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• Resumen

  • n the information age, continuous streams of data from connected devices require intelligent models that ensure security, privacy and transparency. Federated learning enables knowledge sharing while adhering to the principles of trustworthy AI.

    This work synthesizes the Trustworthy and Explainable Federated System for Extracting Descriptive Rules in a Data Streaming Environment (TEFeS-SDR) [9] study, which introduces an evolutionary single-objective federated system for extracting descriptive rules while prioritizing privacy and security through advanced encryption techniques (binary, symmetric, and asymmetric). It ensures traceability and transparency, and experimental results confirm its resilience to concept drift while maintaining high quality models, advancing responsible AI by integrating explainability, security and efficiency.