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

04/30/2021
by   Matei Moldoveanu, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset