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 …

With the increasing number of connected vehicles on the road, vehicular communications
have become an important research area. Federated learning (FL), a distributed machine …

The metaverse, envisioned as the next digital frontier for avatar-based virtual interaction,
involves high-performance models. In this dynamic environment, users' tasks frequently shift …

Deep learning has been used for optimizing a multitude of wireless problems. Yet most
existing works assume that training and test samples are drawn from the same distribution …

In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-
aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is …

The rapid growth of the Internet of Things fosters collaboration among connected devices for
tasks like indoor localization. However, existing indoor localization solutions struggle with …

Federated learning (FL) allows model training from local data by edge devices while
preserving data privacy. However, the learning accuracy decreases due to the heterogeneity …

Traditional Machine Learning (ML) methods often centralize data on a single server for
processing and analysis, which poses significant risks in terms of data privacy. In contrast …

In recent years, the field of distributed deep learning within the Internet of Things (IoT) or the
edge has experienced exponential growth. Federated meta-learning has emerged as a …