INNOCENTI , Lucia

Machine Unlearning (MU) is an emerging discipline studying methods to remove the effect of a data instance on the parameters of a trained model. Federated Unlearning (FU) extends MU to unlearn the contribution of a dataset provided by a client wishing to drop from a federated learning study. Due to the emerging nature of FU, a practical...

Healthcare data is often split into medium/small-sized collections across multiple hospitals and access to it is encumbered by privacy regulations. This brings difficulties to use them for the development of machine learning and deep learning models, which are known to be data-hungry. One way to overcome this limitation is to use collaborative...

The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not...