  In this paper, we compare three privacy-preserving distributed learning
techniques: federated learning, split learning, and SplitFed. We use these
techniques to develop binary classification models for detecting tuberculosis
from chest X-rays and compare them in terms of classification performance,
communication and computational costs, and training time. We propose a novel
distributed learning architecture called SplitFedv3, which performs better than
split learning and SplitFedv2 in our experiments. We also propose alternate
mini-batch training, a new training technique for split learning, that performs
better than alternate client training, where clients take turns to train a
model.
