Selective Updates and Adaptive Masking for Communication-Efficient Federated Learning
A Herzog, R Southamy, O Belarbiyx… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Federated Learning (FL) is fast becoming one of the most prevalent distributed learning
techniques focused on privacy preservation and communication efficiency for large-scale …
techniques focused on privacy preservation and communication efficiency for large-scale …
Joint optimization of data sampling and user selection for federated learning in the mobile edge computing systems
Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …
high quality models without collecting the local private data of users. As a distributed …
A graph neural network learning approach to optimize RIS-assisted federated learning
Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …
intelligence paradigm, where over-the-air computation enables spectral-efficient model …
Semi-Asynchronous Federated Edge Learning for Over-the-Air Computation
Over-the-air Computation (AirComp) has been demonstrated as an effective transmission
scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL …
scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL …
On in-network learning. A comparative study with federated and split learning
M Moldoveanu, A Zaidi - 2021 IEEE 22nd International …, 2021 - ieeexplore.ieee.org
In this paper, we consider a problem in which distributively extracted features are used for
performing inference in wireless networks. We elaborate on our proposed architecture …
performing inference in wireless networks. We elaborate on our proposed architecture …
Resource allocation for wireless federated edge learning based on data importance
The implementation of artificial intelligence (AI) in wireless networks is becoming more and
more popular because of the growing number of mobile devices and the availability of huge …
more popular because of the growing number of mobile devices and the availability of huge …
Simultaneous federated learning and information transmission over time-varying MIMO channels
Wireless federated learning enables many Internet of Things (IoT) devices to perform
collaborative learning under the coordination of the base station (BS). Besides, the BS is …
collaborative learning under the coordination of the base station (BS). Besides, the BS is …
Optimal MIMO combining for blind federated edge learning with gradient sparsification
We provide the optimal receive combining strategy for federated learning in multiple-input
multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform …
multiple-output (MIMO) systems. Our proposed algorithm allows the clients to perform …
Data and channel-adaptive sensor scheduling for federated edge learning via over-the-air gradient aggregation
Over-the-air gradient aggregation and data-aware scheduling have recently drawn great
attention due to the outstanding performance in improving communication efficiency for …
attention due to the outstanding performance in improving communication efficiency for …
Faster Convergence on Heterogeneous Federated Edge Learning: An Adaptive Sidelink-Assisted Data Multicasting Approach
G Hu, Y Teng, N Wang, Z Han - arXiv preprint arXiv:2406.09776, 2024 - arxiv.org
Federated Edge Learning (FEEL) emerges as a pioneering distributed machine learning
paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) …
paradigm for the 6G Hyper-Connectivity, harnessing data from the Internet of Things (IoT) …