Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
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 …
A survey on federated learning: The journey from centralized to distributed on-site learning and beyond
S AbdulRahman, H Tout… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
Driven by privacy concerns and the visions of deep learning, the last four years have
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
witnessed a paradigm shift in the applicability mechanism of machine learning (ML). An …
Communication-efficient federated learning
Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …
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 …
FedMCCS: Multicriteria client selection model for optimal IoT federated learning
S AbdulRahman, H Tout, A Mourad… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
As an alternative centralized systems, which may prevent data to be stored in a central
repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game …
repository due to its privacy and/or abundance, federated learning (FL) is nowadays a game …
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 …
Node selection toward faster convergence for federated learning on non-iid data
Federated Learning (FL) is a distributed learning paradigm that enables a large number of
resource-limited nodes to collaboratively train a model without data sharing. The non …
resource-limited nodes to collaboratively train a model without data sharing. The non …
Relay-assisted federated edge learning: performance analysis and system optimization
In this paper, we study a relay-assisted federated edge learning (FEEL) network under
latency and bandwidth constraints. In this network, users collaboratively train a global model …
latency and bandwidth constraints. In this network, users collaboratively train a global model …