Hierarchical federated learning across heterogeneous cellular networks
We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively
learn a global model by sharing local updates on the model parameters rather than their …
learn a global model by sharing local updates on the model parameters rather than their …
Gradient statistics aware power control for over-the-air federated learning in fading channels
N Zhang, M Tao - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
To enable communication-efficient federated learning, fast model aggregation can be
designed using over-the-air computation (AirComp). In order to implement a reliable and …
designed using over-the-air computation (AirComp). In order to implement a reliable and …
Edge federated learning via unit-modulus over-the-air computation
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 …
from distributed datasets based on wireless communications. This paper proposes a unit …
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 …
Clustered federated learning with model integration for non-iid data in wireless networks
As a typical distributed learning paradigm, federated learning has enabled network edge
intelligence by making full use of the local data and the computing resources at edge …
intelligence by making full use of the local data and the computing resources at edge …
Importance-aware data selection and resource allocation in federated edge learning system
The implementation of artificial intelligence (AI) in wireless networks is becoming more and
more popular because of the growing number of mobile devices and the availability of huge …
more popular because of the growing number of mobile devices and the availability of huge …
Hybrid Learning: When Centralized Learning Meets Federated Learning in the Mobile Edge Computing Systems
Federated learning is a new artificial intelligence technology with which an edge server can
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
orchestrate with multiple end users to train a global model collaboratively. Under this setting …
Joint Pre-Equalization and Receiver Combining Design for Federated Learning with Misaligned Over-the-Air Computation
J Wang, S Guo - IEEE Open Journal of the Communications …, 2023 - ieeexplore.ieee.org
With the growth of terminal devices and data traffic, privacy concerns have inspired an
innovative edge learning framework, called federated learning (FL). Over-the-air …
innovative edge learning framework, called federated learning (FL). Over-the-air …
Knowledge-guided learning for transceiver design in over-the-air federated learning
In this paper, we consider communication-efficient over-the-air federated learning (FL),
where multiple edge devices with non-independent and identically distributed datasets …
where multiple edge devices with non-independent and identically distributed datasets …
SlimFL: Federated learning with superposition coding over slimmable neural networks
Federated learning (FL) is a key enabler for efficient communication and computing,
leveraging devices' distributed computing capabilities. However, applying FL in practice is …
leveraging devices' distributed computing capabilities. However, applying FL in practice is …