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 …
Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6G
Pushing artificial intelligence (AI) from central cloud to network edge has reached board
consensus in both industry and academia for materializing the vision of artificial intelligence …
consensus in both industry and academia for materializing the vision of artificial intelligence …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
What is semantic communication? A view on conveying meaning in the era of machine intelligence
In the 1940s, Claude Shannon developed the information theory focusing on quantifying the
maximum data rate that can be supported by a communication channel. Guided by this …
maximum data rate that can be supported by a communication channel. Guided by this …
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 …
Federated learning via intelligent reflecting surface
Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving
fast model aggregation by exploiting the waveform superposition property of multiple-access …
fast model aggregation by exploiting the waveform superposition property of multiple-access …
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 …
Optimized power control design for over-the-air federated edge learning
Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …
solution to enable distributed machine learning over edge devices by using their data locally …
Interference management for over-the-air federated learning in multi-cell wireless networks
Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …
much attention. However, most existing studies consider one FL task in single-cell wireless …
Dynamic scheduling for over-the-air federated edge learning with energy constraints
Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …