ETD

• explainable ai
• federated learning
• fuzzy logic
• rule-based systems
• trustworthy ai

This thesis addresses the growing need for Trustworthy AI, focusing on the transparency and privacy requirements. Transparency is commonly addressed through eXplainable AI (XAI), while privacy-preserving paradigms such as Federated Learning (FL) enable collaborative model training without sharing sensitive data.
The work extends traditional Fuzzy Rule-Based Classifiers (FRBCs) to federated settings, systematically analyzing their learning behavior and performance under heterogeneous and non-IID data distributions. To further improve the trade-off between predictive accuracy and interpretability, a multi-objective federated framework based on Evolutionary Fuzzy Systems is introduced, using synthetic data generation techniques to support privacy-preserving server-side optimization. In addition to model design, the thesis investigates security aspects by analyzing classical adversarial threats in FL, such as data and model poisoning, and by proposing a taxonomy of attacks specifically targeting federated FRBCs. Finally, an edge-oriented application framework compliant with Multi-Access Edge Computing architectures is presented, demonstrating the practical feasibility and effectiveness of deploying trustworthy, transparent, and privacy-aware AI systems in real-world distributed environments.