X Cao, G Zhu, J Xu, S Cui - IEEE Journal on Selected Areas in …, 2022 - ieeexplore.ieee.org
Over-the-air computation (AirComp) has emerged as a new analog power-domain non- orthogonal multiple access (NOMA) technique for low-latency model/gradient-updates …
Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit …
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 …
In this paper, we consider communication-efficient over-the-air federated learning (FL), where multiple edge devices with non-independent and identically distributed datasets …
G Zhu, Y Du, D Gündüz, K Huang - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point acting …
Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …
X Cao, G Zhu, J Xu, Z Wang… - IEEE Journal on Selected …, 2021 - ieeexplore.ieee.org
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 …
T Sery, N Shlezinger, K Cohen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
We focus on over-the-air (OTA) Federated Learning (FL), which has been suggested recently to reduce the communication overhead of FL due to the repeated transmissions of …