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 …
Federated learning via inexact ADMM
One of the crucial issues in federated learning is how to develop efficient optimization
algorithms. Most of the current ones require full device participation and/or impose strong …
algorithms. Most of the current ones require full device participation and/or impose strong …
CFD: Communication-efficient federated distillation via soft-label quantization and delta coding
Communication constraints are one of the majorchallenges preventing the wide-spread
adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new …
adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new …
Energy-efficient resource allocation for federated learning in noma-enabled and relay-assisted internet of things networks
MS Al-Abiad, MZ Hassan… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
Distributed machine learning (ML) algorithms are imperative for the next-generation Internet
of Things (IoT) networks, thanks to preserving the privacy of users' data and efficient usage …
of Things (IoT) networks, thanks to preserving the privacy of users' data and efficient usage …
Harnessing wireless channels for scalable and privacy-preserving federated learning
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …
wireless channels bring challenges for model training, in which channel randomness …
Communication-efficient ADMM-based federated learning
Federated learning has shown its advances over the last few years but is facing many
challenges, such as how algorithms save communication resources, how they reduce …
challenges, such as how algorithms save communication resources, how they reduce …
Communication-efficient ADMM-based distributed algorithms for sparse training
G Wang, Y Lei, Y Qiu, L Lou, Y Li - Neurocomputing, 2023 - Elsevier
In large-scale distributed machine learning (DML), the synchronization efficiency of the
distributed algorithm becomes a critical factor that affects the training time of machine …
distributed algorithm becomes a critical factor that affects the training time of machine …
Learning, computing, and trustworthiness in intelligent IOT environments: Performance-energy tradeoffs
An Intelligent IoT Environment (iIoTe) is comprised of heterogeneous devices that can
collaboratively execute semi-autonomous IoT applications, examples of which include …
collaboratively execute semi-autonomous IoT applications, examples of which include …
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM
This paper explores the challenges of implementing Federated Learning (FL) in practical
scenarios featuring isolated nodes with data heterogeneity, which can only be connected to …
scenarios featuring isolated nodes with data heterogeneity, which can only be connected to …
Energy efficient federated learning in integrated fog-cloud computing enabled internet-of-things networks
MS Al-Abiad, MZ Hassan, MJ Hossain - arXiv preprint arXiv:2107.03520, 2021 - arxiv.org
We investigate resource allocation scheme to reduce the energy consumption of federated
learning (FL) in the integrated fog-cloud computing enabled Internet-of-things (IoT) …
learning (FL) in the integrated fog-cloud computing enabled Internet-of-things (IoT) …