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 …
Threshold-based data exclusion approach for energy-efficient federated edge learning
Federated edge learning (FEEL) is a promising distributed learning technique for next-
generation wireless networks. FEEL preserves the user's privacy, reduces the …
generation wireless networks. FEEL preserves the user's privacy, reduces 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 …
Performance-oriented design for intelligent reflecting surface-assisted federated learning
To efficiently exploit the massive amounts of raw data that are increasingly being generated
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …
in mobile edge networks, federated learning (FL) has emerged as a promising distributed …
Reconfigurable intelligent surface enabled federated learning: A unified communication-learning design approach
To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …
CSIT-free model aggregation for federated edge learning via reconfigurable intelligent surface
We study over-the-air model aggregation in federated edge learning (FEEL) systems, where
channel state information at the transmitters (CSIT) is assumed to be unavailable. We …
channel state information at the transmitters (CSIT) is assumed to be unavailable. We …
A graph neural network learning approach to optimize RIS-assisted federated learning
Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …
intelligence paradigm, where over-the-air computation enables spectral-efficient model …
Gradient and channel aware dynamic scheduling for over-the-air computation in federated edge learning systems
To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …
Time-correlated sparsification for efficient over-the-air model aggregation in wireless federated learning
Federated edge learning (FEEL) is a promising distributed machine learning (ML)
framework to drive edge intelligence applications. However, due to the dynamic wireless …
framework to drive edge intelligence applications. However, due to the dynamic wireless …
Intelligent reflecting surface-assisted low-latency federated learning over wireless networks
Federated learning (FL) is an emerging technique to support privacy-aware and resource-
constrained machine learning, where a base station (BS) will coordinate a set of distributed …
constrained machine learning, where a base station (BS) will coordinate a set of distributed …