The global 6G vision has taken its shape after years of international research and
development efforts. This work culminated in ITU-R's Recommendation on" IMT-2030 …

Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …

To alleviate the substantial local training burden on clients in the federated learning (FL)
process, this paper proposes a more efficient approach based on hybrid federated and …

The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …

Federated learning emerges as a solution to the dilemma of data silos while safeguarding
data privacy, particularly relevant in the consumer electronics sector where user data privacy …

Federated learning enables model training on multiple clients locally, without the need to
transfer their data to a central server, thus ensuring data privacy. In this paper, we …

The rapid advancement of technology has set higher standards for the next generation of
wireless communication networks, known as 6G. These networks go beyond the simple task …

Currently, most applications in the Industrial Internet of Things (IIoT) still rely on CNN-based
neural networks. Although Transformer-based large models (LMs), including language …

The rapid evolution of user equipment (UE) and 5G networks drives significant
transformations, bringing technology closer to end‐users. Managing resources in densely …

Federated Learning (FL) has emerged as a leading methodology for facilitating collaborative
edge learning (EL) across Artificial Intelligence of Things (AIoT) devices, enabling efficient …