  The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at
improving the performance of practical DL implementations in terms of cloud
bandwidth consumption, response latency, and data privacy preservation.
Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based
classifiers, we present CDC, a Classification Driven Compression framework that
reduces bandwidth consumption while preserving classification accuracy of
edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for
resource-limited edge servers, we develop a lightweight autoencoder with a
classification guidance for compression with classification driven feature
preservation, which allows edges to only upload the latent code of raw data for
accurate global training on the Cloud. Additionally, we design an adjustable
quantization scheme adaptively pursuing the tradeoff between bandwidth
consumption and classification accuracy under different network conditions,
where only fine-tuning is required for rapid compression ratio adjustment.
Results of extensive experiments demonstrate that, compared with DNN training
with raw data, CDC consumes 14.9 times less bandwidth with an accuracy loss no
more than 1.06%, and compared with DNN training with data compressed by AE
without guidance, CDC introduces at least 100% lower accuracy loss.
