The growing number of AI-driven applications in the 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. Combined with compression-aware methods where
learning adapts to compression of communicated data, the benefits of this
approach have further improved and could serve as an alternative to established
approaches like federated learning methods. 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 (use
less network resources and are faster), 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, that 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.

通过适用于边缘云资源的自适应压缩感知裂化学习方法，我们可以改善和训练深度学习模型，使其在网络上更加高效，并通过变换学习方法扩展训练，以换取更高效的推理能力，而不损失准确性。