Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones Are Difficult to Beat

Erdong Hu, Yuxin Tang, Anastasios Kyrillidis, Chris Jermaine

Abstract:

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.