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 …
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 …
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 …
Q Lan, D Wen, Z Zhang, Q Zeng, X Chen… - Journal of …, 2021 - ieeexplore.ieee.org
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 …
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 …
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 …
Z Wang, J Qiu, Y Zhou, Y Shi, L Fu… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
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 …
H Wu, P Wang - IEEE Transactions on Network Science and …, 2022 - ieeexplore.ieee.org
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 …
L Chen, L Fan, X Lei, TQ Duong… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
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 …