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

María Asunción Padilla-Rascón, Ángel Miguel García Vico, Cristóbal José Carmona del Jesús

• 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.