In recent years, quantum computing (QC) has been getting a lot of attention from industry
and academia. Especially, among various QC research topics, variational quantum circuit …

With its privacy-preserving and decentralized features, distributed learning plays an
irreplaceable role in the era of wireless networks with a plethora of smart terminals, an …

Although deep learning (DL) has already become a state-of-the-art technology for various
data processing tasks, data security and computational overload problems often arise due to …

Mobile edge computing (MEC) brings computational resources to the edge of network that
triggers the paradigm shift of centralized machine learning towards federated learning …

Split learning (SL) is a promising distributed learning framework that enables to utilize the
huge data and parallel computing resources of mobile devices. SL is built upon a model …

The participatory nature of federated learning (FL) makes it attractive for fingerprinting-based
indoor localization in multibuilding and multifloor environments. A group of sensing clients …

For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used
based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms …

The advancing applications based on machine learning and deep learning in
communication networks have been exponentially increasing in the system architectures of …

Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet
wireless channels bring challenges for model training, in which channel randomness …

Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …