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.

翻译：在本文中,我们考虑了一个在无线网络中进行推论时使用分配提取的特征的问题。我们详细阐述了我们拟议中的架构,我们在此称之为“网络内学习 ”, 提供了适当的损失功能,并讨论了利用神经网络进行优化的问题。我们将其性能与联邦和分解学习进行比较,并表明这一架构既能提供更好的准确性,又能节省带宽。