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 …
From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare
Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …
collaboration across multiple centres while ensuring data privacy and security are not …
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 …
Federated machine learning for skin lesion diagnosis: an asynchronous and weighted approach
The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening
disease. However, the implementation of traditional machine learning algorithms in …
disease. However, the implementation of traditional machine learning algorithms in …
Towards federated transfer learning in electrocardiogram signal analysis
Modern methods in artificial intelligence perform very well on many healthcare datasets, at
times outperforming trained doctors. However, many assumptions made in model training …
times outperforming trained doctors. However, many assumptions made in model training …
Federated learning stability under byzantine attacks
Federated Learning (FL) is a machine learning approach that enables private and
decentralized model training. Although FL has been shown to be very useful in several …
decentralized model training. Although FL has been shown to be very useful in several …
An efficient and private ecg classification system using split and semi-supervised learning
Electrocardiography (ECG) is a standard diagnostic tool for evaluating the overall heart's
electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG …
electrical activity and is vital for detecting many cardiovascular diseases. Classifying ECG …
Federated learning: A cutting-edge survey of the latest advancements and applications
Robust machine learning (ML) models can be developed by leveraging large volumes of
data and distributing the computational tasks across numerous devices or servers …
data and distributing the computational tasks across numerous devices or servers …
Deep learning models for magnetic cardiography edge sensors implementing noise processing and diagnostics
Remote health monitoring has become a necessity due to reduced healthcare access
resulting from pandemic lockdowns and the increasing aging population …
resulting from pandemic lockdowns and the increasing aging population …
Communication-efficient federated learning in drone-assisted IoT networks: Path planning and enhanced knowledge distillation techniques
As 5G and beyond networks continue to proliferate, intelligent monitoring systems are
becoming increasingly prevalent. However, geographically isolated regions with sparse …
becoming increasingly prevalent. However, geographically isolated regions with sparse …