Blockchain-based two-stage federated learning with non-IID data in IoMT system
The Internet of Medical Things (IoMT) has a bright future with the development of smart
mobile devices. Information technology is also leading changes in the healthcare industry …
mobile devices. Information technology is also leading changes in the healthcare industry …
A comprehensive survey on client selection strategies in federated learning
Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …
training while preserving data privacy. Client selection plays a crucial role in determining the …
Distance-aware hierarchical federated learning in blockchain-enabled edge computing network
Federated learning (FL) has been proposed as an emerging paradigm to perform privacy-
preserving distributed machine learning in the Internet of Things (IoT). However, the …
preserving distributed machine learning in the Internet of Things (IoT). However, the …
Non-iid data in federated learning: A systematic review with taxonomy, metrics, methods, frameworks and future directions
Recent advances in machine learning have highlighted Federated Learning (FL) as a
promising approach that enables multiple distributed users (so-called clients) to collectively …
promising approach that enables multiple distributed users (so-called clients) to collectively …
[HTML][HTML] Adaptive single-layer aggregation framework for energy-efficient and privacy-preserving load forecasting in heterogeneous Federated smart grids
Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …
data from distributed load networks while ensuring data privacy. However, the …
Federated learning with non-iid data: A survey
Z Lu, H Pan, Y Dai, X Si, Y Zhang - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …
processing nonindependent and identically distributed (non-IID) data due to geographical …
Towards efficient asynchronous federated learning in heterogeneous edge environments
Federated learning (FL) is widely used in edge environments as a privacy-preserving
collaborative learning paradigm. However, edge devices often have heterogeneous …
collaborative learning paradigm. However, edge devices often have heterogeneous …
Privacy-preserving clustering federated learning for non-IID data
G Luo, N Chen, J He, B **, Z Zhang, Y Li - Future Generation Computer …, 2024 - Elsevier
With the increasing number of intelligent devices joining into the Internet of Things (IoT),
traditional centralized learning struggles to meet the performance requirements of terminal …
traditional centralized learning struggles to meet the performance requirements of terminal …
Heterogeneous privacy level-based client selection for hybrid federated and centralized learning in mobile edge computing
To alleviate the substantial local training burden on clients in the federated learning (FL)
process, this paper proposes a more efficient approach based on hybrid federated and …
process, this paper proposes a more efficient approach based on hybrid federated and …
A survey of federated learning from data perspective in the healthcare domain: Challenges, methods, and future directions
Recent advances in deep learning (DL) have shown that data-driven insights can be used in
smart healthcare applications to improve the quality of life for patients. DL needs more data …
smart healthcare applications to improve the quality of life for patients. DL needs more data …