This paper presents the use of federated learning (FL) in healthcare to improve the efficiency
and accuracy of medical diagnosis while addressing privacy concerns related to medical …

Federated learning (FL) has emerged as a promising approach for training machine
learning models on distributed data while preserving privacy specifically in the field of …

Data-driven models often require a large amount of data for sufficient training. Federated
learning (FL) is a machine learning framework that can effectively help multiple sites utilize …

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 …

Deep learning-based medical image analysis is an effective and precise method for
identifying various cancer types. However, due to concerns over patient privacy, sharing …

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 …

Advances have been made in the field of Machine Learning showing that it is an effective
tool that can be used for solving real world problems. This success is hugely attributed to the …

Medical institutions' internal data is closely related to individual privacy information, and has
a high degree of confidentiality and sensitivity in data storage and data use. Federated …

Artificial intelligence (AI) holds great promise in healthcare as it can significantly advances
disease diagnosis using diverse medical datasets. However, learning generalizable …

Federated learning (FL), also referred to as collaborative learning, uses a number of
dispersed edge devices or servers to run the training algorithms, without exchanging local …