Distributed deep learning (DL) plays a critical role in many wireless Internet of Things (IoT)
applications including remote camera deployment. This work addresses three practical
challenges in cyber-deployment of distributed DL over band-limited channels. Specifically, many
IoT systems consist of sensor nodes for raw data collection and encoding, and servers for
learning and inference tasks. Adaptation of DL over band-limited network data links has only
been scantly addressed. A second challenge is the need for pre-deployed encoders being
compatible with flexible decoders that can be upgraded or retrained. The third challenge is the
robustness against erroneous training labels. Addressing these three challenges, we develop a
hierarchical learning strategy to improve image classification accuracy over band-limited links
between sensor nodes and servers. Experimental results show that our hierarchically trained
models can improve link spectrum efficiency without performance loss, reduce storage and
computational complexity, and achieve robustness against training label corruption.
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