  The growing number of AI-driven applications in mobile devices has led to
solutions that integrate deep learning models with the available edge-cloud
resources. Due to multiple benefits such as reduction in on-device energy
consumption, improved latency, improved network usage, and certain privacy
improvements, split learning, where deep learning models are split away from
the mobile device and computed in a distributed manner, has become an
extensively explored topic. Incorporating compression-aware methods (where
learning adapts to compression level of the communicated data) has made split
learning even more advantageous. This method could even offer a viable
alternative to traditional methods, such as federated learning techniques. In
this work, we develop an adaptive compression-aware split learning method
('deprune') to improve and train deep learning models so that they are much
more network-efficient, which would make them ideal to deploy in weaker devices
with the help of edge-cloud resources. This method is also extended ('prune')
to very quickly train deep learning models through a transfer learning
approach, which trades off little accuracy for much more network-efficient
inference abilities. We show that the 'deprune' method can reduce network usage
by 4x when compared with a split-learning approach (that does not use our
method) without loss of accuracy, while also improving accuracy over
compression-aware split-learning by 4 percent. Lastly, we show that the 'prune'
method can reduce the training time for certain models by up to 6x without
affecting the accuracy when compared against a compression-aware split-learning
approach.
