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Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
A comprehensive review on federated learning based models for healthcare applications
A disease is an abnormal condition that negatively impacts the functioning of the human
body. Pathology determines the causes behind the disease and identifies its development …
body. Pathology determines the causes behind the disease and identifies its development …
[HTML][HTML] Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains
The continuous emergence of new and sophisticated malware specifically targeting Android-
based Internet of Things devices is causing significant security hazards and is consequently …
based Internet of Things devices is causing significant security hazards and is consequently …
[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 …
Reinforcement learning for intelligent healthcare systems: A review of challenges, applications, and open research issues
The rise of chronic disease patients and the pandemic pose immediate threats to healthcare
expenditure and mortality rates. This calls for transforming healthcare systems away from …
expenditure and mortality rates. This calls for transforming healthcare systems away from …
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 …
Smart sampling: Hel** from friendly neighbors for decentralized federated learning
Federated Learning (FL) is gaining widespread interest for its ability to share knowledge
while preserving privacy and reducing communication costs. Unlike Centralized FL …
while preserving privacy and reducing communication costs. Unlike Centralized FL …
Evaluation of the trade-off between performance and communication costs in federated learning scenario
Abstract Background and Objective: In traditional Machine Learning (ML) approaches, the
data are collected and stored by a single node and subsequently used for training and …
data are collected and stored by a single node and subsequently used for training and …
Recent methodological advances in federated learning for healthcare
For healthcare datasets, it is often impossible to combine data samples from multiple sites
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …
due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of …
AiFed: An adaptive and integrated mechanism for asynchronous federated data mining
With the growing concerns on datasecurity and user privacy, a decentralized mechanism is
implemented for federated data mining (FDM), which can bridge data silos and collaborate …
implemented for federated data mining (FDM), which can bridge data silos and collaborate …