00601nas a2200133   4500008004100000020002200041245013300063210006900196260001200265100001900277700001900296700002500315856012700340       2024                        eng d  a978-989-758-709-200aEnhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection0 aEnhancing Privacy and Utility in Federated Learning A Hybrid P2P  bINSTICC1 aCorbucci, Luca1 aMonreale, Anna1 aPellungrini, Roberto  uhttps://kdd.isti.cnr.it/publications/enhancing-privacy-and-utility-federated-learning-hybrid-p2p-and-server-based-approach00468nas a2200133   4500008004100000245007500041210006900116100001900185700002300204700002700227700001900254700002400273856003700297       2024                        eng d00aPUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning0 aPUFFLE Balancing Privacy Utility and Fairness in Federated Learn1 aCorbucci, Luca1 aHeikkila, Mikko, A1 aNoguero, David, Solans1 aMonreale, Anna1 aKourtellis, Nicolas  uhttps://arxiv.org/abs/2407.1522401467nas a2200145   4500008004100000020002200041245004900063210004800112260003800160520097900198100001901177700002301196700001901219856008301238       2023                        eng d  a978-3-031-44067-000aExplaining Black-Boxes in Federated Learning0 aExplaining BlackBoxes in Federated Learning  aChambSpringer Nature Switzerland3 aFederated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance's prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.1 aCorbucci, Luca1 aGuidotti, Riccardo1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/explaining-black-boxes-federated-learning02096nas a2200157   4500008004100000245005900041210005900100520156600159100001901725700001901744700002301763700002101786700002201807700002001829856008901849       2022                        eng d00aSemantic Enrichment of XAI Explanations for Healthcare0 aSemantic Enrichment of XAI Explanations for Healthcare3 aExplaining black-box models decisions is crucial to increase doctors' trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems.1 aCorbucci, Luca1 aMonreale, Anna1 aPanigutti, Cecilia1 aNatilli, Michela1 aSmiraglio, Simona1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/semantic-enrichment-xai-explanations-healthcare