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 …
A Chowdhury, H Kassem, N Padoy, R Umeton… - International MICCAI …, 2021 - Springer
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 …
Federated learning (FL) enables collaborative training of a machine learning (ML) model across multiple parties, facilitating the preservation of users' and institutions' privacy by …
L Sun, A Karagulyan… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Abstract Stein Variational Gradient Descent (SVGD) is an important alternative to the Langevin-type algorithms for sampling from probability distributions of the form $\pi …
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the …
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 …
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 …