  We carefully evaluate a number of algorithms for learning in a federated
environment, and test their utility for a variety of image classification
tasks. We consider many issues that have not been adequately considered before:
whether learning over data sets that do not have diverse sets of images affects
the results; whether to use a pre-trained feature extraction "backbone"; how to
evaluate learner performance (we argue that classification accuracy is not
enough), among others. Overall, across a wide variety of settings, we find that
vertically decomposing a neural network seems to give the best results, and
outperforms more standard reconciliation-used methods.
