Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

Subgradient Descent Learning Over Fading Multiple Access Channels With Over-the-Air Computation

We focus on a distributed learning problem in a communication network, consisting of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> distributed nodes and a central parameter server (PS). The PS is responsible for performing the computat...

Full description

Bibliographic Details
Main Authors: Tamir L. S. Gez, Kobi Cohen
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Distributed learning
gradient descent (GD)-type learning
subgradient methods
federated learning (FL)
multiple access channel (MAC)
over-the-air (OTA) computation
Online Access:https://ieeexplore.ieee.org/document/10168898/

Internet

https://ieeexplore.ieee.org/document/10168898/

Similar Items