most citedPrivacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning

2 citations · 2 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2023★ 2 cited

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…

cs.LG2023★ 1 cited

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…