Distributed learning over a wireless network with non-coherent majority vote computation
A Şahin - IEEE Transactions on Wireless Communications, 2023 - ieeexplore.ieee.org
In this study, we propose an over-the-air computation (OAC) scheme to calculate the
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …
majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edge …
Distributed learning over a wireless network with FSK-based majority vote
In this study, we propose an over-the-air computation (AirComp) scheme for federated edge
learning (FEEL). The proposed scheme relies on the concept of distributed learning by …
learning (FEEL). The proposed scheme relies on the concept of distributed learning by …
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 …
Over-the-air computation with DFT-spread OFDM for federated edge learning
In this study, we propose an over-the-air computation (AirComp) scheme for federated edge
learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the …
learning (FEEL) without channel state information (CSI) at the edge devices (EDs) or the …
One-bit over-the-air aggregation for communication-efficient federated edge learning: Design and convergence analysis
Federated edge learning (FEEL) is a popular framework for model training at an edge server
using data distributed at edge devices (eg, smart-phones and sensors) without …
using data distributed at edge devices (eg, smart-phones and sensors) without …
Chirp-based over-the-air computation for long-range federated edge learning
In this study, we propose circularly-shifted chirp (CSC)-based majority vote (MV)(CSC-MV),
a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated …
a power-efficient over-the-air computation (OAC) scheme, to achieve long-range federated …
Chirp-based majority vote computation for federated edge learning and distributed localization
In this study, we propose an over-the-air computation (OAC) scheme based on chirps to
detect the majority votes (MVs) in a wireless network for federated edge learning (FEEL) and …
detect the majority votes (MVs) in a wireless network for federated edge learning (FEEL) and …
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 …
Federated edge learning with misaligned over-the-air computation
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …
in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …
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 …