8 citations · 15 across the 4 of their papers we have counts for
4 papers
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning
Gergely Dániel Németh, Miguel Ángel Lozano, Novi Quadrianto +1
Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning, particularly in heterogeneous FL settings where clients have varying co…
Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
Ahmed Begga, Francisco Escolano, Miguel Angel Lozano +1
High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the…
A Snapshot of the Frontiers of Client Selection in Federated Learning
Gergely Dániel Németh, Miguel Ángel Lozano, Novi Quadrianto +1
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a num…
BIASeD: Bringing Irrationality into Automated System Design
Aditya Gulati, Miguel Angel Lozano, Bruno Lepri +1
Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such bia…