The rapid advancement of artificial intelligence technologies has given rise to diversified
intelligent services, which place unprecedented demands on massive connectivity and …

The development of applications based on artificial intelligence and implemented over
wireless networks is increasingly rapidly and is expected to grow dramatically in the future …

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a
mass of edge devices to collaboratively train a global model while preserving privacy. In this …

Federated learning (FL) is recognized as a promising privacy-preserving distributed
machine learning paradigm, given its potential to enable collaborative model training among …

The reconfigurable intelligent surface (RIS), which is composed of multiple passive reflective
components, is now considered as an effective mean to improve security performance in …

To have uninterrupted wireless connectivity, higher throughput, and latency rate down to
nanoseconds; future networks will rely heavily on higher frequency bands, yet these signals …

For the purpose of training a machine learning model via exploiting data from multiple
devices without compromising their privacy, federated learning (FL) has become a popular …

Over-the-air computation (AirComp) based federated learning (FL) has been a promising
technique for distilling artificial intelligence (AI) at the network edge. However, the …

The development of six generation (6G) wireless communication technology is becoming
increasingly imminent to achieve internet of everything (IoE) in today's digital era …

Next-generation wireless networks will enable to support applications in various domains
including smart factories, intelligent transportation, e-health, and more. Therefore, future …