Ensemble distillation based adaptive quantization for supporting federated learning in wireless...

Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey

M Le, T Huynh-The, T Do-Duy, TH Vu… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
被引用次数：5 相关文章
[PDF] arxiv.org

Applications of distributed machine learning for the Internet-of-Things: A comprehensive survey

M Le, T Huynh-The, T Do-Duy, TH Vu… - arXiv preprint arXiv …, 2023 - arxiv.org
The emergence of new services and applications in emerging wireless networks (eg,
beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) …
被引用次数：7 相关文章 所有 2 个版本

Two-layer optimization with utility game and resource control for federated learning in edge networks

F Tian, X Zhang, X Wang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a distributed machine learning paradigm that can be organized in
two layers. In the outer layer of users, there is a model interaction process between the task …
被引用次数：2 相关文章 所有 3 个版本
[PDF] arxiv.org

Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks

Y Lin, K Wang, Z Ding - arXiv preprint arXiv:2403.03157, 2024 - arxiv.org
This study explores the benefits of integrating the novel clustered federated learning (CFL)
approach with non-orthogonal multiple access (NOMA) under non-independent and …
被引用次数：3 相关文章 所有 2 个版本
[PDF] arxiv.org

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization

X Han, W Chen, J Li, M Ding, Q Wu, K Wei… - arXiv preprint arXiv …, 2024 - arxiv.org
Federated learning (FL) has been recognized as a viable distributed learning paradigm for
training a machine learning model across distributed clients without uploading raw data …
被引用次数：1 相关文章 所有 2 个版本

Sub-channel assignment and power allocation in NOMA-enhanced federated learning networks

Y Lin, K Wang, Z Ding - 2024 IEEE 99th Vehicular Technology …, 2024 - ieeexplore.ieee.org
Although Federated Learning (FL) has garnered increasing attention from researchers, the
development of ad-vanced FL frameworks incorporating multiple access techniques remains …
被引用次数：1 相关文章

Adaptive Clustering based Straggler-aware Federated Learning in Wireless Edge Networks

YJ Liu, G Feng, H Du, Z Qin, Y Sun… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Federated learning (FL) has been vigorously promoted in wireless edge networks as it
fosters collaborative training of machine learning (ML) models while preserving individual …
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AQUILA: Communication Efficient Federated Learning With Adaptive Quantization in Device Selection Strategy

Z Zhao, Y Mao, Z Shi, Y Liu, T Lan… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed
learning methodology, has been impeded by the challenge of high communication …
被引用次数：5 相关文章 所有 3 个版本
[PDF] arxiv.org

Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions

L Qin, T Zhu, W Zhou, PS Yu - arXiv preprint arXiv:2406.10861, 2024 - arxiv.org
Federated Learning (FL) is a distributed and privacy-preserving machine learning paradigm
that coordinates multiple clients to train a model while keeping the raw data localized …
被引用次数：2 相关文章

Communication-Efficient Personalized Federated Learning for Green Communications in IoMT

Z Chen, J Du, C Jiang, Y Lu… - ICC 2024-IEEE …, 2024 - ieeexplore.ieee.org
The rapid development of the Internet of Medical Things (IoMT) has brought about an
enormous amount of healthcare data. Effectively and securely processing this sensitive data …
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