Guardians of the Deep Fog

Abstract

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.

Enabling and Leveraging AI in the Intelligent Edge: A Review of Current Trends and Future Directions

“…Moving up to the level of reliable AI using IoT data, deep-FogGuard [123] is a DNN augmentation scheme which makes distributed inference resilient to failure. The main feature of this scheme is that it relies on skip hyperconnections, which function like residual connections in DNNs, except that they skip entire nodes rather than simply layers.…”