Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) has attracted increasing attention to building models without
accessing raw user data, especially in healthcare. In real applications, different federations …

There is a growing interest in applying machine learning techniques to healthcare. Recently,
federated machine learning (FL) is gaining popularity since it allows researchers to train …

Human activity recognition (HAR) is a time series classification task that focuses on
identifying the motion patterns from human sensor readings. Adequate data is essential but …

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing,
analysing behaviours through multi-dimensional observations. Despite research progress …

Human Activity Recognition (HAR) models often suffer from performance degradation in real-
world applications due to distribution shifts in activity patterns across individuals. Test-Time …

Human activity recognition (HAR) is one of the most promising technologies in the smart
home, especially radio frequency (RF-based) method, which has the advantages of low cost …

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key
task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and …

Human activity recognition using sensor data has become a research hotspot in the field of
ubiquitous computing and has a wide range of application scenarios in real life. Effectively …