The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Communication and computation are often viewed as separate tasks. This approach is very
effective from the perspective of engineering as isolated optimizations can be performed …

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving
fast model aggregation by exploiting the waveform superposition property of multiple-access …

Machine learning and wireless communication technologies are jointly facilitating an
intelligent edge, where federated edge learning (FEEL) is emerging as a promising training …

The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

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

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data
aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition …

Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …

We study the collaborative image retrieval problem at the wireless edge, where multiple
edge devices capture images of the same object from different angles and locations, which …

Semantic communication is an emerging research area that has gained a wide range of
attention recently. Despite this growing interest, there remains a notable absence of a …