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Distributed learning in wireless networks: Recent progress and future challenges
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …
applications to efficiently analyze various types of data collected by edge devices for …
Multiple access techniques for intelligent and multifunctional 6G: Tutorial, survey, and outlook
Multiple access (MA) is a crucial part of any wireless system and refers to techniques that
make use of the resource dimensions (eg, time, frequency, power, antenna, code, and …
make use of the resource dimensions (eg, time, frequency, power, antenna, code, and …
Broadband analog aggregation for low-latency federated edge learning
To leverage rich data distributed at the network edge, a new machine-learning paradigm,
called edge learning, has emerged where learning algorithms are deployed at the edge for …
called edge learning, has emerged where learning algorithms are deployed at the edge for …
A survey on over-the-air computation
Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …
effective from the perspective of engineering as isolated optimizations can be performed …
Federated learning via over-the-air computation
The stringent requirements for low-latency and privacy of the emerging high-stake
applications with intelligent devices such as drones and smart vehicles make the cloud …
applications with intelligent devices such as drones and smart vehicles make the cloud …
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 …
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 …
Federated learning: A signal processing perspective
T Gafni, N Shlezinger, K Cohen… - IEEE Signal …, 2022 - ieeexplore.ieee.org
The dramatic success of deep learning is largely due to the availability of data. Data
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …
samples are often acquired on edge devices, such as smartphones, vehicles, and sensors …
Towards massive connectivity support for scalable mMTC communications in 5G networks
C Bockelmann, NK Pratas, G Wunder, S Saur… - IEEE …, 2018 - ieeexplore.ieee.org
The fifth generation of cellular communication systems is foreseen to enable a multitude of
new applications and use cases with very different requirements. A new 5G multi-service air …
new applications and use cases with very different requirements. A new 5G multi-service air …
Over-the-air computation systems: Optimization, analysis and scaling laws
For future Internet-of-Things based Big Data applications, data collection from ubiquitous
smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to …
smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to …