Privacy-Preserving Federated Interpretability
Interpretability has become a crucial component in the Machine Learning (ML) domain. This is particularly important in the context of medical and health applications, where the underlying reasons behind how an ML model makes a certain decision are as important as the decision itself for the experts. However, interpreting an ML model based on limited local data may potentially lead to inaccurate co
