Distributed learning in wireless networks: Recent progress and future challenges

M Chen, D Gündüz, K Huang, W Saad… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

[HTML][HTML] Federated learning for 6G: Applications, challenges, and opportunities

Z Yang, M Chen, KK Wong, HV Poor, S Cui - Engineering, 2022 - Elsevier
Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …

Joint device scheduling and resource allocation for latency constrained wireless federated learning

W Shi, S Zhou, Z Niu, M Jiang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
In federated learning (FL), devices contribute to the global training by uploading their local
model updates via wireless channels. Due to limited computation and communication …

Client selection in federated learning: Principles, challenges, and opportunities

L Fu, H Zhang, G Gao, M Zhang… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …

Fast-convergent federated learning

HT Nguyen, V Sehwag… - IEEE Journal on …, 2020 - ieeexplore.ieee.org
Federated learning has emerged recently as a promising solution for distributing machine
learning tasks through modern networks of mobile devices. Recent studies have obtained …

A systematic literature review on federated machine learning: From a software engineering perspective

SK Lo, Q Lu, C Wang, HY Paik, L Zhu - ACM Computing Surveys (CSUR …, 2021 - dl.acm.org
Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

Machine learning for large-scale optimization in 6g wireless networks

Y Shi, L Lian, Y Shi, Z Wang, Y Zhou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

Gradient and channel aware dynamic scheduling for over-the-air computation in federated edge learning systems

J Du, B Jiang, C Jiang, Y Shi… - IEEE Journal on Selected …, 2023 - ieeexplore.ieee.org
To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

Applications of federated learning; taxonomy, challenges, and research trends

M Shaheen, MS Farooq, T Umer, BS Kim - Electronics, 2022 - mdpi.com
The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Node selection toward faster convergence for federated learning on non-iid data

H Wu, P Wang - IEEE Transactions on Network Science and …, 2022 - ieeexplore.ieee.org
Federated Learning (FL) is a distributed learning paradigm that enables a large number of
resource-limited nodes to collaboratively train a model without data sharing. The non …