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 …
Multi-agent federated reinforcement learning for resource allocation in uav-enabled internet of medical things networks
In the 5G/B5G network paradigms, intelligent medical devices known as the Internet of
Medical Things (IoMT) have been used in the healthcare industry to monitor remote users' …
Medical Things (IoMT) have been used in the healthcare industry to monitor remote users' …
Energy-efficient federated learning with resource allocation for green IoT edge intelligence in B5G
An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
accelerate the response of IoT services by deploying edge intelligence near IoT devices …
Client selection approach in support of clustered federated learning over wireless edge networks
Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to
obtain reliable specialized models when data is imbalanced and distributed in a non-iid …
obtain reliable specialized models when data is imbalanced and distributed in a non-iid …
Semi-supervised federated learning over heterogeneous wireless iot edge networks: Framework and algorithms
Federated learning (FL) is a promising paradigm for future sixth-generation wireless systems
to underpin network edge intelligence for smart cities applications. However, most of the …
to underpin network edge intelligence for smart cities applications. However, most of the …
Fair selection of edge nodes to participate in clustered federated multitask learning
Clustered federated Multitask learning is introduced as an efficient technique when data is
unbalanced and distributed amongst clients in a non-independent and identically distributed …
unbalanced and distributed amongst clients in a non-independent and identically distributed …
A survey of energy-efficient strategies for federated learning inmobile edge computing
K Yan, N Shu, T Wu, C Liu, P Yang - Frontiers of Information Technology & …, 2024 - Springer
With the booming development of fifth-generation network technology and Internet of Things,
the number of end-user devices (EDs) and diverse applications is surging, resulting in …
the number of end-user devices (EDs) and diverse applications is surging, resulting in …
Privacy-preserving honeypot-based detector in smart grid networks: A new design for quality-assurance and fair incentives federated learning framework
A Albaseer, M Abdallah - 2023 IEEE 20th Consumer …, 2023 - ieeexplore.ieee.org
Adopting honeypot defenses is a promising technology for protecting the industrial Internet
of Things (IIoT), particularly the Advanced Metering Infrastructure (AMI). The effectiveness of …
of Things (IIoT), particularly the Advanced Metering Infrastructure (AMI). The effectiveness of …
Data-driven participant selection and bandwidth allocation for heterogeneous federated edge learning
Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-
generation wireless edge systems. Smart systems across various application domains face …
generation wireless edge systems. Smart systems across various application domains face …
Asynchronous Federated Learning for Resource Allocation in Software Defined Internet of UAVs
KI Qureshi, L Wang, X Xiong… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
The use of Unmanned Aerial Vehicles (UAVs) as flying base stations to support various
tasks, such as data collection, machine learning (ML) model training, and wireless …
tasks, such as data collection, machine learning (ML) model training, and wireless …