On In-network learning. A Comparative Study with Federated and Split Learning

Abstract

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.

Accepted for publication at the 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), special session on Machine learning at the Edge. arXiv admin note: substantial text overlap with arXiv:2107.03433