Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

This paper proposes a scheme addressing the challenges of integrating privacy-preserving
distributed machine learning in the Internet of Things (IoT) context while improving the …

Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather
immense data via onboard sensors and mobile applications. This offers an amazing …

Machine learning (ML) is a promising enabler for the fifth generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

As a privacy-preserving paradigm of decentralized machine learning, federated learning
(FL) has become a hot spot in the field of machine learning. Existing FL approaches …

With increasingly strict data privacy regulations, federated learning (FL) has become one of
the most often heard machine learning techniques due to its privacy-preserving trait. To …

Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of
wireless systems, such as sixth-generation (6G) mobile network. However, massive data …

With the proliferation of distributed edge computing resources, the 6G mobile network will
evolve into a network for connected intelligence. Along this line, the proposal to incorporate …

Federated Learning (FL) algorithms commonly sample a random subset of clients to address
the straggler issue and improve communication efficiency. While recent works have …

In the era of advanced technologies, mobile devices are equipped with computing and
sensing capabilities that gather excessive amounts of data. These amounts of data are …