A systematic review of federated learning from clients' perspective: challenges and solutions
Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …
processing by allowing clients to train intermediate models on their devices with locally …
Toward energy-efficient federated learning over 5G+ mobile devices
The continuous convergence of machine learning algorithms, 5G and beyond (5G+)
wireless communications, and artificial intelligence (AI) hardware implementation hastens …
wireless communications, and artificial intelligence (AI) hardware implementation hastens …
Green concerns in federated learning over 6G
As Information, Communications, and Data Technology (ICDT) are deeply integrated, the
research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial …
research of 6G gradually rises. Meanwhile, federated learning (FL) as a distributed artificial …
Energy and distribution-aware cooperative clustering algorithm in Internet of Things (IoT)-based federated learning
J Lee, H Ko - IEEE Transactions on Vehicular Technology, 2023 - ieeexplore.ieee.org
In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from
the base station (BS), they consume lots of energy to transmit the updated parameters to BS …
the base station (BS), they consume lots of energy to transmit the updated parameters to BS …
Privacy vs. efficiency: Achieving both through adaptive hierarchical federated learning
As a decentralized training paradigm, Federated learning (FL) promises data privacy by
exchanging model parameters instead of raw local data. However, it is still impeded by the …
exchanging model parameters instead of raw local data. However, it is still impeded by the …
Quantized distributed federated learning for industrial internet of things
T Ma, H Wang, C Li - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
Federated learning (FL) enables multiple devices to collaboratively train a shared machine
learning (ML) model while keeping all the local data private, which is a crucial enabler to …
learning (ML) model while keeping all the local data private, which is a crucial enabler to …
AC-DNN: An Adaptive Compact DNNs Architecture for Collaborative Learning Among Heterogeneous Smart Devices
G Wu, F Liu, S Li, Q Song, Z Tang - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
With the rapid development of the Internet of Things (IoT), a massive number of smart
devices are deployed in industry and critical infrastructures. Nowadays, IoT smart devices …
devices are deployed in industry and critical infrastructures. Nowadays, IoT smart devices …
[PDF][PDF] A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning.
Asynchronous federated learning (AsynFL) can effectively mitigate the impact of
heterogeneity of edge nodes on joint training while satisfying participant user privacy …
heterogeneity of edge nodes on joint training while satisfying participant user privacy …