Update aware device scheduling for federated learning at the wireless edge
We study federated learning (FL) at the wireless edge, where power-limited devices with
local datasets train a joint model with the help of a remote parameter server (PS). We …
local datasets train a joint model with the help of a remote parameter server (PS). We …
Federated learning with non-IID data in wireless networks
Federated learning provides a promising paradigm to enable network edge intelligence in
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …
the future sixth generation (6G) systems. However, due to the high dynamics of wireless …
Communication-efficient and distributed learning over wireless networks: Principles and applications
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …
Joint client selection and bandwidth allocation algorithm for federated learning
In federated learning (FL), if the participating mobile devices have low computing power and
poor wireless channel conditions and/or they do not have sufficient data for various classes …
poor wireless channel conditions and/or they do not have sufficient data for various classes …
Accelerating DNN training in wireless federated edge learning systems
Training task in classical machine learning models, such as deep neural networks, is
generally implemented at a remote cloud center for centralized learning, which is typically …
generally implemented at a remote cloud center for centralized learning, which is typically …
Adaptive configuration for heterogeneous participants in decentralized federated learning
Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices
Recent advances in machine learning, wireless communication, and mobile hardware
technologies promisingly enable federated learning (FL) over massive mobile edge devices …
technologies promisingly enable federated learning (FL) over massive mobile edge devices …
A joint learning and communications framework for federated learning over wireless networks
In this article, the problem of training federated learning (FL) algorithms over a realistic
wireless network is studied. In the considered model, wireless users execute an FL …
wireless network is studied. In the considered model, wireless users execute an FL …
Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems
The communication and networking field is hungry for machine learning decision-making
solutions to replace the traditional model-driven approaches that proved to be not rich …
solutions to replace the traditional model-driven approaches that proved to be not rich …
GoMORE: Global model reuse for resource-constrained wireless federated learning
Due to the dynamics of wireless channels and limited wireless resources (ie, spectrum),
deploying federated learning (FL) over wireless networks is challenged by frequent FL …
deploying federated learning (FL) over wireless networks is challenged by frequent FL …