Federated learning (FL) is a new technology that has been a hot research topic. It enables the training of an algorithm across multiple decentralized edge devices or servers holding …
Healthcare is predominantly regarded as a crucial consideration in promoting the general physical and mental health and well‐being of people around the world. The amount of data …
S Banabilah, M Aloqaily, E Alsayed, N Malik… - Information processing & …, 2022 - Elsevier
Federated Learning (FL) has been foundational in improving the performance of a wide range of applications since it was first introduced by Google. Some of the most prominent …
Y Kumar, R Singla - … Learning Systems: Towards Next-Generation AI, 2021 - Springer
In the medical or healthcare industry, where, the already available information or data is never sufficient, excellence can be performed with the help of Federated Learning (FL) by …
Due to the fast advancement of artificial intelligence (AI), centralized-based models have become critical for healthcare tasks like in medical image analysis and human behavior …
The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the …
The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in …
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing …