The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

The fifth generation (5G) and beyond wireless networks are envisioned to provide an
integrated communication and computing platform that will enable multipurpose and …

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 …

In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

In a vertical federated learning (VFL) scenario where features and models are split into
different parties, it has been shown that sample-level gradient information can be exploited …

Federated learning has emerged as a popular technique for distributing machine learning
(ML) model training across the wireless edge. In this paper, we propose two timescale …

Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …

Federated learning has generated significant interest, with nearly all works focused on a
“star” topology where nodes/devices are each connected to a central server. We migrate …

Federated edge learning (FEEL) is a popular distributed learning framework that allows
privacy-preserving collaborative model training via periodic learning-updates …