Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Background The proliferation of mobile health (mHealth) applications is partly driven by the
advancements in sensing and communication technologies, as well as the integration of …

As an essential approach in many Internet of Things (IoT) applications, multiview learning
synthesizes multiple features to achieve more comprehensive descriptions of data items …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

Industrial cloud computing and Internet of Things have transformed the healthcare industry
with the rapid growth of distributed healthcare data. Security and privacy of healthcare data …

The fast growth of pre-trained models (PTMs) has brought natural language processing to a
new era, which has become a dominant technique for various natural language processing …

With the increasing data scale in the Industrial Internet of Things, edge computing
coordinated with machine learning is regarded as an effective way to raise the novel latency …

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based
deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL …

Wind energy is a fast-growing renewable energy but faces blade icing. Data-driven methods
provide talented solutions for blade icing detection, but a considerable amount of Internet of …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …