On In-network learning. A Comparative Study with Federated and Split Learning
Machine Learning
2021-09-20 v2 Information Theory
Machine Learning
math.IT
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.
Cite
@article{arxiv.2104.14929,
title = {On In-network learning. A Comparative Study with Federated and Split Learning},
author = {Matei Moldoveanu and Abdellatif Zaidi},
journal= {arXiv preprint arXiv:2104.14929},
year = {2021}
}
Comments
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