• clustering
• distributed systems
• explainable artificial intelligence
• federated learning
• fuzzy logic
• machine learning

This thesis proposes novel approaches for the design and the implementation of trustworthy AI systems, considering both the unsupervised and supervised learning paradigms; it focuses on traditional ML models, less covered in the specialized literature compared to cutting-edge Deep Learning ones. As per unsupervised learning, a novel approach to execute the privacy-preserving C-Means and Fuzzy C-Means algorithms over decentralized data is presented, addressing both the horizontal and vertical data partitioning patterns. As per supervised learning, federated approaches are proposed for learning Takagi-Sugeno-Kang Fuzzy Rule-based Systems and Fuzzy Regression Trees, generally acknowledged as transparent or highly interpretable-by-design models for regression problems. This work gives an original contribution to the novel field of Fed-XAI (Federated Learning of eXplainable AI models) because it simultaneously addresses the requirements of privacy preservation and explainability, representing indeed a leap forward toward trustworthy AI. The contribution is not limited to algorithmic aspects: in fact, an extension of an existing open-source framework (named OpenFL) is also presented, aimed to support the implementation of Fed-XAI models. Such an extension, named OpenFL-XAI, provides a convenient method for creating AI applications balancing accuracy, privacy, and interpretability.