01467nas 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-learning