Utility fairness for the differentially private federated-learning-based wireless IoT networks

SA Alvi, Y Hong, S Durrani - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Federated learning (FL) allows predictive model training on the sensed data in a wireless
Internet of Things (IoT) network evading data collection cost in terms of energy, time, and …
被引用次数：7 相关文章 所有 2 个版本
[PDF] arxiv.org

Utility fairness for the differentially private federated learning

SA Alvi, Y Hong, S Durrani - arXiv preprint arXiv:2109.05267, 2021 - arxiv.org
Federated learning (FL) allows predictive model training on the sensed data in a wireless
Internet of things (IoT) network evading data collection cost in terms of energy, time, and …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

Federated learning cost disparity for IoT devices

SA Alvi, Y Hong, S Durrani - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Federated learning (FL) promotes predictive model training at the Internet of things (IoT)
devices by evading data collection cost in terms of energy, time, and privacy. We model the …
被引用次数：3 相关文章 所有 4 个版本
[PDF] nsf.gov

Incentivizing differentially private federated learning: A multidimensional contract approach

M Wu, D Ye, J Ding, Y Guo, R Yu… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning is a promising tool in the Internet-of-Things (IoT) domain for training a
machine learning model in a decentralized manner. Specifically, the data owners (eg, IoT …
被引用次数：92 相关文章 所有 3 个版本

Jointly optimizing client selection and resource management in wireless federated learning for internet of things

L Yu, R Albelaihi, X Sun, N Ansari… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed
machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients …
被引用次数：94 相关文章 所有 3 个版本
[PDF] arxiv.org

Clustered data sharing for Non-IID federated learning over wireless networks

G Hu, Y Teng, N Wang, FR Yu - ICC 2023-IEEE International …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) is a novel distributed machine learning approach to leverage data
from Internet of Things (IoT) devices while maintaining data privacy. However, the current FL …
被引用次数：4 相关文章 所有 3 个版本
[PDF] arxiv.org

Learner Referral for Cost-Effective Federated Learning Over Hierarchical IoT Networks

Y Gao, Z Ye, Y Xiao, W Xiang - arXiv preprint arXiv:2307.09977, 2023 - arxiv.org
The paradigm of federated learning (FL) to address data privacy concerns by locally training
parameters on resource-constrained clients in a distributed manner has garnered significant …
相关文章 所有 2 个版本

Toward Smart and Efficient Service Systems: Computational Layered Federated Learning Framework

Y Shi, X Li, S Chen - IEEE Network, 2023 - ieeexplore.ieee.org
As increasing concerns have arisen on privacy leakage in data-driven smart services,
federated learning (FL) has been introduced to collaboratively learn an efficient model …
被引用次数：4 相关文章

Privacy-preserving asynchronous grouped federated learning for IoT

T Zhang, A Song, X Dong, Y Shen… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
Federated learning (FL), a cooperative distributed learning framework, has been employed
in various intelligent Internet of Things (IoT) applications (eg, smart health-care, smart home …
被引用次数：25 相关文章

Semi-federated learning for connected intelligence with computing-heterogeneous devices

J Han, W Ni, L Li - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a promising distributed learning approach which enables multiple
devices to collaboratively train deep neural networks in a privacy-preserving fashion …
被引用次数：2 相关文章