Heterogeneous computation and resource allocation for wireless powered federated edge learning systems
Federated learning (FL) is a popular edge learning approach that utilizes local data and
computing resources of network edge devices to train machine learning (ML) models while …
computing resources of network edge devices to train machine learning (ML) models while …
Wirelessly powered federated edge learning: Optimal tradeoffs between convergence and power transfer
Q Zeng, Y Du, K Huang - IEEE Transactions on Wireless …, 2021 - ieeexplore.ieee.org
Federated edge learning (FEEL) is a widely adopted framework for training an artificial
intelligence (AI) model distributively at edge devices to leverage their data while preserving …
intelligence (AI) model distributively at edge devices to leverage their data while preserving …
Federated cooperation and augmentation for power allocation in decentralized wireless networks
Emerging mobile edge techniques and applications such as Augmented Reality (AR)/Virtual
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …
Reality (VR), Internet of Things (IoT), and vehicle networking, result in an explosive growth of …
Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing
Q Zeng, Y Du, K Huang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Edge machine learning involves the deployment of learning algorithms at the network edge
to leverage massive distributed data and computation resources to train artificial intelligence …
to leverage massive distributed data and computation resources to train artificial intelligence …
Energy-efficient federated learning over UAV-enabled wireless powered communications
Since the invention in 2016, federated learning (FL) has been a key concept of artificial
intelligence, in which the data of FL users needs not to be uploaded to the central server …
intelligence, in which the data of FL users needs not to be uploaded to the central server …
Energy-aware resource management for federated learning in multi-access edge computing systems
In Federated Learning (FL), a global statistical model is developed by encouraging mobile
users to perform the model training on their local data and aggregating the output local …
users to perform the model training on their local data and aggregating the output local …
Min-max cost optimization for efficient hierarchical federated learning in wireless edge networks
Federated learning is a distributed machine learning technology that can protect users' data
privacy, so it has attracted more and more attention in the industry and academia …
privacy, so it has attracted more and more attention in the industry and academia …
Energy-efficient federated edge learning with joint communication and computation design
This paper studies a federated edge learning system, in which an edge server coordinates a
set of edge devices to train a shared machine learning (ML) model based on their locally …
set of edge devices to train a shared machine learning (ML) model based on their locally …
UAV communications for sustainable federated learning
Federated learning (FL), invented by Google in 2016, has become a hot research trend.
However, enabling FL in wireless networks has to overcome the limited battery challenge of …
However, enabling FL in wireless networks has to overcome the limited battery challenge of …
Fine-grained data selection for improved energy efficiency of federated edge learning
In Federated edge learning (FEEL), energy-constrained devices at the network edge
consume significant energy when training and uploading their local machine learning …
consume significant energy when training and uploading their local machine learning …