In the era of sixth-generation (6G) wireless communications, integrated sensing and
communications (ISAC) is recognized as a promising solution to upgrade the physical …

With the growing awareness of data privacy, federated learning (FL) has gained increasing
attention in recent years as a major paradigm for training models with privacy protection in …

Intelligent transport systems are increasingly being used in practice these days. Fog nodes
and cloud servers collect real-time pedestrian and vehicle data and train them based on …

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as
an exhaustive search among all candidate beam pairs cannot be assuredly completed …

In healthcare, machine learning (ML) shows significant potential to augment patient care,
improve population health, and streamline healthcare workflows. Realizing its full potential …

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 …

The increasing availability of multimodal data holds many promises for developments in
millimeter-wave (mmWave) multiple-antenna systems by harnessing the potential for …

Federated learning (FL) is a distributed machine learning (ML) paradigm that enables clients
to collaborate without accessing, infringing upon, or leaking original user data by sharing …

Creating a digital world that closely mimics the real world with its many complex interactions
and outcomes is possible today through advanced emulation software and ubiquitous …