Short-term load forecasting (STLF), which typically ranges from hour-ahead to a few days ahead, plays a multitude of critical roles.

Local treatment effects are a common quantity found throughout the empirical sciences that measure the treatment effect among those who comply with what they are assigned. Most of the literature is focused on estimating the average of such quantity, which is called the " local average treatment effect (LATE) " [

In Table 1, we present the results on the Federated EMNIST (FEMNIST)16 dataset which isone of therealisticfederated learning datasets in the literature (see the paper by Caldas etal. In this experiments, for IFCA and one-shot clustering algorithm, we share the representation layers among all the20 models, but the last layers for different models are trained based on clustering. As we can see, the results of IFCA21 areonparwith theone-shot clustering algorithm. In our experiments, we observe that our algorithm is robust to the choice of27 numberofclusters.