Recent advances in communication technologies and the Internet-of-Medical-Things (IOMT)
have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI …

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 …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

The thriving of artificial intelligence (AI) applications is driving the further evolution of
wireless networks. It has been envisioned that 6G will be transformative and will …

In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …

Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial
participation) when the number of participants is large and the server's communication …

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as
a promising paradigm for dealing with the massive amounts of data generated by intelligent …

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that
make use of the resource dimensions (eg, time, frequency, power, antenna, code, and …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Standard machine-learning approaches involve the centralization of training data in a data
center, where centralized machine-learning algorithms can be applied for data analysis and …