Subgradient descent learning over fading multiple access channels with over-the-air computation
TLS Gez, K Cohen - IEEE Access, 2023 - ieeexplore.ieee.org
We focus on a distributed learning problem in a communication network, consisting of
distributed nodes and a central parameter server (PS). The PS is responsible for performing …
distributed nodes and a central parameter server (PS). The PS is responsible for performing …
Subgradient descent learning with over-the-air computation
TLS Gez, K Cohen - ICASSP 2023-2023 IEEE International …, 2023 - ieeexplore.ieee.org
We consider a distributed learning problem in a communication network, consisting of N
distributed nodes and a central parameter server (PS). The computation is made by the PS …
distributed nodes and a central parameter server (PS). The computation is made by the PS …
Accelerated gradient descent learning over multiple access fading channels
R Paul, Y Friedman, K Cohen - IEEE Journal on Selected Areas …, 2021 - ieeexplore.ieee.org
We consider a distributed learning problem in a wireless network, consisting of distributed
edge devices and a parameter server (PS). The objective function is a sum of the edge …
edge devices and a parameter server (PS). The objective function is a sum of the edge …
On analog gradient descent learning over multiple access fading channels
T Sery, K Cohen - IEEE Transactions on Signal Processing, 2020 - ieeexplore.ieee.org
We consider a distributed learning problem over multiple access channel (MAC) using a
large wireless network. The computation is made by the network edge and is based on …
large wireless network. The computation is made by the network edge and is based on …
A sequential gradient-based multiple access for distributed learning over fading channels
T Sery, K Cohen - 2019 57th Annual Allerton Conference on …, 2019 - ieeexplore.ieee.org
A distributed learning problem over multiple access channel (MAC) using a large wireless
network is considered. The objective function is a sum of the nodes' local loss functions. The …
network is considered. The objective function is a sum of the nodes' local loss functions. The …
Federated learning over wireless fading channels
We study federated machine learning at the wireless network edge, where limited power
wireless devices, each with its own dataset, build a joint model with the help of a remote …
wireless devices, each with its own dataset, build a joint model with the help of a remote …
Joint optimization for federated learning over the air
In this paper, we focus on federated learning (FL) over the air based on analog aggregation
transmission in realistic wireless networks. We first derive a closed-form expression for the …
transmission in realistic wireless networks. We first derive a closed-form expression for the …
Fast convergence algorithm for analog federated learning
In this paper, we consider federated learning (FL) over a noisy fading multiple access
channel (MAC), where an edge server aggregates the local models transmitted by multiple …
channel (MAC), where an edge server aggregates the local models transmitted by multiple …
CHARLES: Channel-quality-adaptive over-the-air federated learning over wireless networks
Over-the-air federated learning (OTA-FL) has emerged as an efficient mechanism that
exploits the superposition property of the wireless medium and performs model aggregation …
exploits the superposition property of the wireless medium and performs model aggregation …
Machine learning at the wireless edge: Distributed stochastic gradient descent over-the-air
We study federated machine learning (ML) at the wireless edge, where power-and
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …
bandwidth-limited wireless devices with local datasets carry out distributed stochastic …