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 …

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 …

Federated learning aims to learn a global model that performs well on client devices with
limited cross-client communication. Personalized federated learning (PFL) further extends …

Graph unlearning, which involves deleting graph elements such as nodes, node labels, and
relationships from a trained graph neural network (GNN) model, is crucial for real-world …

Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …

Abstract Stein Variational Gradient Descent (SVGD) is an important alternative to the
Langevin-type algorithms for sampling from probability distributions of the form $\pi …

Abstract The objective of Federated Learning (FL) is to perform statistical inference for data
which are decentralised and stored locally on networked clients. FL raises many constraints …

Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure,
communication, computing and learning in a privacy-preserving manner. However, the …

Abstract Stein Variational Gradient Descent (SVGD) is an algorithm for sampling from a
target density which is known up to a multiplicative constant. Although SVGD is a popular …

Distributed Stein Variational Gradient Descent (DSVGD) is a non-parametric distributed
learning framework for federated Bayesian learning, where multiple clients jointly train a …