G Zhu, Y Wang, K Huang - IEEE Transactions on Wireless …, 2019 - ieeexplore.ieee.org
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 …
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 …
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 …
Y Shao, D Gündüz, SC Liew - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel …
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 …
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 …
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 …
In this paper, we develop an orthogonal frequency-division multiplexing (OFDM)-based over- the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local …
Machine learning and wireless communication technologies are jointly facilitating an intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …