Semantic Enrichment of Explanations of AI Models for Healthcare. - Luca Corbucci; Anna Monreale; Cecilia Panigutti; Michela Natilli; Simona Smiraglio; Dino Pedreschi (DS, 2023)

Explaining Black-Boxes in Federated Learning. - Luca Corbucci; Riccardo Guidotti; Anna Monreale (xAI (2), 2023)

PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning. - Luca Corbucci; Mikko A. Heikkilä; David Solans Noguero; Anna Monreale; Nicolas Kourtellis (ECAI, 2024)

Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection. - Luca Corbucci; Anna Monreale; Roberto Pellungrini (SECRYPT, 2024)

A Benchmark for Client-level Fairness in Federated Learning. - Xenia Heilmann; Luca Corbucci; Mattia Cerrato (EWAF, 2025)

Benefits of the Federation? Analyzing the Impact of Fair Federated Learning at the Client Level. - Luca Corbucci; Xenia Heilmann; Mattia Cerrato (FAccT, 2025)

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation. - Xenia Heilmann; Luca Corbucci; Mattia Cerrato; Anna Monreale (CoRR, 2025)

An Interpretable Data-Driven Unsupervised Approach for the Prevention of Forgotten Items. - Luca Corbucci; Javier Alejandro Borges Legrottaglie; Francesco Spinnato; Anna Monreale; Riccardo Guidotti (CoRR, 2025)