Federated learning is a promising learning paradigm that allows collaborative training of
models across multiple data owners without sharing their raw datasets. To enhance privacy …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Recently, there has been a surge in the development of advanced intelligent generative
content (AIGC), especially large language models (LLMs). However, for many downstream …

Human activity recognition (HAR) is a key enabling technology for a wide range of emerging
applications. Although multimodal sensing systems are essential for capturing complex and …

Multi-modal sensing systems are increasingly prevalent in real-world applications such as
health monitoring and autonomous driving. Most multi-modal learning approaches need to …

The increasing concern for privacy and the use of machine learning on personal data has
led researchers to introduce new approaches to machine learning. Federated learning is …

Human activity recognition (HAR) using wearable sensors enables continuous monitoring
for healthcare applications. However, the conventional centralised training of deep learning …

Recently, surface electromyographic (sEMG) hand gesture recognition faces a serious
challenge of limited training data in various scenarios. Numerous efforts have been made to …

Federated Learning (FL) enhances predictive accuracy in load forecasting by integrating
data from distributed load networks while ensuring data privacy. However, the …

Human-robot collaboration is a promising solution to relieve construction workers from
repetitive and physically demanding tasks, thus improving construction safety and …