Federated Learning (FL) has received attention due to its data privacy-preserving mechanism. As a decentralized Machine Learning framework, all clients collectively train a model and a server gathers the model parameters to form a global model. Every client can apply the global model to tackle...

Model diagnostics and forecast evaluation are closely related tasks, with the former concerning in-sample goodness (or lack) of fit and the latter addressing predictive performance out-of-sample. We review the ubiquitous setting in which forecasts are cast in the form of quantiles or...