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 …
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 …
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 …
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 …
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 …
Over-the-air decentralized federated learning
In this paper, we consider decentralized federated learning (FL) over wireless networks,
where over-the-air computation (AirComp) is adopted to facilitate the local model consensus …
where over-the-air computation (AirComp) is adopted to facilitate the local model consensus …
One bit aggregation for federated edge learning with reconfigurable intelligent surface: Analysis and optimization
H Li, R Wang, W Zhang, J Wu - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
As one of the most popular and attractive frameworks for model training, federated edge
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …
learning (FEEL) presents a new paradigm, which avoids direct data transmission by …
Over-the-air federated learning with retransmissions
H Hellström, V Fodor… - 2021 IEEE 22nd …, 2021 - ieeexplore.ieee.org
Federated Learning (FL) is a distributed machine learning technique designed to utilize the
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …
distributed datasets collected by our mobile and internet-of-things devices. As such, it is …
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 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 …