Joint parameter-and-bandwidth allocation for improving the efficiency of partitioned edge learning
To leverage data and computation capabilities of mobile devices, machine learning
algorithms are deployed at the network edge for training artificial intelligence (AI) models …
algorithms are deployed at the network edge for training artificial intelligence (AI) models …
Resource allocation for multiuser edge inference with batching and early exiting
The deployment of inference services at the network edge, called edge inference, offloads
computation-intensive inference tasks from mobile devices to edge servers, thereby …
computation-intensive inference tasks from mobile devices to edge servers, thereby …
Optimizing resource allocation for joint AI model training and task inference in edge intelligence systems
This letter considers an edge intelligence system where multiple end users (EUs)
collaboratively train an artificial intelligence (AI) model under the coordination of an edge …
collaboratively train an artificial intelligence (AI) model under the coordination of an edge …
Edge learning for large-scale Internet of Things with task-oriented efficient communication
In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent
applications and services. As the network size becomes large, different users may generate …
applications and services. As the network size becomes large, different users may generate …
Towards Resource-Efficient Edge AI: From Federated Learning to Semi-Supervised Model Personalization
A central question in edge intelligence is “how can an edge device learn its local model with
limited data and constrained computing capacity?” In this study, we explore the approach …
limited data and constrained computing capacity?” In this study, we explore the approach …
Energy-efficient radio resource allocation for federated edge learning
Q Zeng, Y Du, K Huang… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …
to leverage massive distributed data and computation resources. Among others, the …
AceFL: Federated learning accelerating in 6G-enabled mobile edge computing networks
6G is envisioned to achieve ubiquitous Artificial Intelligence (AI) in heterogeneous and
massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to …
massive-scale networks, where FEderated Edge Learning (FEEL) is an effective way to …
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 …
Broadband analog aggregation for low-latency federated edge learning (extended version)
The popularity of mobile devices results in the availability of enormous data and
computational resources at the network edge. To leverage the data and resources, a new …
computational resources at the network edge. To leverage the data and resources, a new …
Learning centric wireless resource allocation for edge computing: Algorithm and experiment
Edge intelligence is an emerging network architecture that integrates sensing,
communication, computing components, and supports various machine learning …
communication, computing components, and supports various machine learning …