The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core
The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …
integrated communication and computing platform that will enable multipurpose and …
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 …
High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks
Federated edge learning (FEEL) technology for vehicular networks is considered as a
promising technology to reduce the computation workload while keeping the privacy of …
promising technology to reduce the computation workload while keeping the privacy of …
AutoFL: a Bayesian game approach for autonomous client participation in federated edge learning
Given that devices (ie, clients) participating in federated edge learning (FEL) are
autonomous and resource-constrained in nature, it is critical to design effective incentive …
autonomous and resource-constrained in nature, it is critical to design effective incentive …
Federated learning resource optimization and client selection for total energy minimization under outage, latency, and bandwidth constraints with partial or no CSI
MH Mahmoud, A Albaseer, M Abdallah… - IEEE Open Journal of …, 2023 - ieeexplore.ieee.org
We consider the problem of minimizing the total energy consumption due to the computation
and communication tasks of federated learning (FL) under bandwidth and latency …
and communication tasks of federated learning (FL) under bandwidth and latency …
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 …