The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Machine learning typically relies on the assumption that training and testing distributions are
identical and that data is centrally stored for training and testing. However, in real-world …

In recent years, federated learning (FL) has become a very popular paradigm for training
distributed, large-scale, and privacy-preserving machine learning (ML) systems. In contrast …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

U-shaped networks have become prevalent in various medical image tasks such as
segmentation, and restoration. However, most existing U-shaped networks rely on …

With the expansion of intelligent services and applications powered by Artificial Intelligence
(AI), the Internet of Things (IoT) permeates many aspects of our everyday lives. In order to …

With the rapid development of emerging network technologies such as cloud computing, the
background server-side of public health information services is widely deployed on the …

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 …

Federated Learning (FL), which enables multiple clients to cooperatively train global models
without revealing private data, has gained significant attention from researchers in recent …