Beamforming vector design and device selection in over-the-air federated learning
In this paper, we consider a beamforming vector design and device selection problem in
over-the-air computation (AirComp) for federated learning. Since the learning performance …
over-the-air computation (AirComp) for federated learning. Since the learning performance …
Communication-efficient stochastic zeroth-order optimization for federated learning
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many
edge devices to collaboratively train a global model without sharing their private data. To …
edge devices to collaboratively train a global model without sharing their private data. To …
Communication efficient federated learning over multiple access channels
In this work, we study the problem of federated learning (FL), where distributed users aim to
jointly train a machine learning model with the help of a parameter server (PS). In each …
jointly train a machine learning model with the help of a parameter server (PS). In each …
Over-the-air computation based on balanced number systems for federated edge learning
A Şahin - IEEE Transactions on Wireless Communications, 2023 - ieeexplore.ieee.org
In this study, we propose a digital over-the-air computation (OAC) scheme for achieving
continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that …
continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that …
Communication-efficient distributed SGD using random access for over-the-air computation
J Choi - IEEE Journal on Selected Areas in Information Theory, 2022 - ieeexplore.ieee.org
In this paper, we study communication-efficient distributed stochastic gradient descent
(SGD) with data sets of users distributed over a certain area and communicating through …
(SGD) with data sets of users distributed over a certain area and communicating through …
Blind federated edge learning
We study federated edge learning (FEEL), where wireless edge devices, each with its own
dataset, learn a global model collaboratively with the help of a wireless access point acting …
dataset, learn a global model collaboratively with the help of a wireless access point acting …
Multi-cell non-coherent over-the-air computation for federated edge learning
In this paper, we propose a framework where over-the-air computation (OAC) occurs in both
uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the …
uplink (UL) and downlink (DL), sequentially, in a multi-cell environment to address the …
Convergence of federated learning over a noisy downlink
We study federated learning (FL), where power-limited wireless devices utilize their local
datasets to collaboratively train a global model with the help of a remote parameter server …
datasets to collaboratively train a global model with the help of a remote parameter server …
Distributed learning with sparsified gradient differences
A very large number of communications are typically required to solve distributed learning
tasks, and this critically limits scalability and convergence speed in wireless communications …
tasks, and this critically limits scalability and convergence speed in wireless communications …
Analog gradient aggregation for federated learning over wireless networks: Customized design and convergence analysis
This article investigates the analog gradient aggregation (AGA) solution to overcome the
communication bottleneck for wireless federated learning applications by exploiting the idea …
communication bottleneck for wireless federated learning applications by exploiting the idea …