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 …

In this white paper we provide a vision for 6G Edge Intelligence. Moving towards 5G and
beyond the future 6G networks, intelligent solutions utilizing data-driven machine learning …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …

Secure model aggregation across many users is a key component of federated learning
systems. The state-of-the-art protocols for secure model aggregation, which are based on …

Communication is widely known as the primary bottleneck of federated learning, and
quantization of local model updates before uploading to the parameter server is an effective …

Newton-type methods are popular in federated learning due to their fast convergence. Still,
they suffer from two main issues, namely: low communication efficiency and low privacy due …

The aim of this paper is to propose a resource allocation strategy for dynamic training and
inference of machine learning tasks at the edge of the wireless network, with the goal of …

The large communication cost for exchanging gradients between different nodes
significantly limits the scalability of distributed training for large-scale learning models …

The end-edge-cloud orchestration of the virtual power plant (VPP) enables the edge server
to timely serve community users. By deploying the community energy storage system …

A very large number of communications are typically required to solve distributed learning
tasks, and this critically limits scalability and convergence speed in wireless communications …