  In this paper, we consider a problem in which distributively extracted
features are used for performing inference in wireless networks. We elaborate
on our proposed architecture, which we herein refer to as "in-network
learning", provide a suitable loss function and discuss its optimization using
neural networks. We compare its performance with both Federated- and Split
learning; and show that this architecture offers both better accuracy and
bandwidth savings.
