Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

In recent years, deep learning models have shown their advantages in neuroimage
analysis, such as brain disease diagnosis. Unfortunately, it is usually difficult to acquire …

Federated learning (FL) is one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …

Purpose This study aims to summarize the critical issues in medical federated learning and
applicable solutions. Also, detailed explanations of how federated learning techniques can …

Digital therapeutics, evidence-based treatments delivered through software programs, are
revolutionizing healthcare by utilizing cutting-edge computing technologies. Unlike …

The increasingly stringent regulations on privacy protection have sparked interest in
federated learning. As a distributed machine learning framework, it bridges isolated data …

The emerging Personalized Federated Learning (PFL) methods aim to produce
personalized models for different users, so as to keep track of their individualized …

Most of the machine learning and artificial intelligence applications are data driven. When it
comes to sensitive data, maintaining the data privacy principles is a big challenge. Building …