Federated Learning of Fuzzy Rule-based Systems: Advances in Privacy, Security and Interpretability

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.