  This paper studies task-oriented edge networks where multiple edge
internet-of-things nodes execute machine learning tasks with the help of
powerful deep neural networks (DNNs) at a network cloud. Separate edge nodes
(ENs) result in a partially observable system where they can only get
partitioned features of the global network states. These local observations
need to be forwarded to the cloud via resource-constrained wireless fronthual
links. Individual ENs compress their local observations into uplink fronthaul
messages using task-oriented encoder DNNs. Then, the cloud carries out a remote
inference task by leveraging received signals. Such a distributed topology
requests a decentralized training and decentralized execution (DTDE) learning
framework for designing edge-cloud cooperative inference rules and their
decentralized training strategies. First, we develop fronthaul-cooperative DNN
architecture along with proper uplink coordination protocols suitable for
wireless fronthaul interconnection. Inspired by the nomographic function, an
efficient cloud inference model becomes an integration of a number of shallow
DNNs. This modulized architecture brings versatile calculations that are
independent of the number of ENs. Next, we present a decentralized training
algorithm of separate edge-cloud DNNs over downlink wireless fronthaul
channels. An appropriate downlink coordination protocol is proposed, which
backpropagates gradient vectors wirelessly from the cloud to the ENs.
