@conference {1597,
	title = {Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection},
	booktitle = {Proceedings of the 21st International Conference on Security and Cryptography - SECRYPT},
	year = {2024},
	publisher = {INSTICC},
	organization = {INSTICC},
	isbn = {978-989-758-709-2},
	doi = {10.5220/0012863600003767},
	author = {Luca Corbucci and Anna Monreale and Roberto Pellungrini}
}
@conference {1596,
	title = {PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning},
	year = {2024},
	url = {https://arxiv.org/abs/2407.15224},
	author = {Luca Corbucci and Mikko A Heikkila and David Solans Noguero and Anna Monreale and Nicolas Kourtellis}
}
@conference {1595,
	title = {Explaining Black-Boxes in Federated Learning},
	booktitle = {Explainable Artificial Intelligence},
	year = {2023},
	publisher = {Springer Nature Switzerland},
	organization = {Springer Nature Switzerland},
	address = {Cham},
	abstract = {Federated 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{\textquoteright}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.},
	isbn = {978-3-031-44067-0},
	author = {Luca Corbucci and Guidotti, Riccardo and Monreale, Anna}
}
@proceedings {1498,
	title = {Semantic Enrichment of XAI Explanations for Healthcare},
	year = {2022},
	abstract = {Explaining black-box models decisions is crucial to increase doctors{\textquoteright} 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{\textquoteright}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.},
	author = {Luca Corbucci and Anna Monreale and Cecilia Panigutti and Michela Natilli and Smiraglio, Simona and Dino Pedreschi}
}
