  Partitioning and distributing deep neural networks (DNNs) over physical nodes
such as edge, fog, or cloud nodes, could enhance sensor fusion, and reduce
bandwidth and inference latency. However, when a DNN is distributed over
physical nodes, failure of the physical nodes causes the failure of the DNN
units that are placed on these nodes. The performance of the inference task
will be unpredictable, and most likely, poor, if the distributed DNN is not
specifically designed and properly trained for failures. Motivated by this, we
introduce deepFogGuard, a DNN architecture augmentation scheme for making the
distributed DNN inference task failure-resilient. To articulate deepFogGuard,
we introduce the elements and a model for the resiliency of distributed DNN
inference. Inspired by the concept of residual connections in DNNs, we
introduce skip hyperconnections in distributed DNNs, which are the basis of
deepFogGuard's design to provide resiliency. Next, our extensive experiments
using two existing datasets for the sensing and vision applications confirm the
ability of deepFogGuard to provide resiliency for distributed DNNs in
edge-cloud networks.
