2 citations · 6 across the 5 of their papers we have counts for
6 papers
DPMM-CFL: Clustered Federated Learning via Dirichlet Process Mixture Model Nonparametric Clustering
Mariona Jaramillo-Civill, Peng Wu, Pau Closas
Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a glo…
A Bayesian Framework for Clustered Federated Learning
Peng Wu, Tales Imbiriba, Pau Closas
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced d…
Robust Interference Mitigation techniques for Direct Position Estimation
Haoqing Li, Shuo Tang, Peng Wu +1
Global Navigation Satellite System (GNSS) is pervasive in navigation and positioning applications, where precise position and time referencing estimations are required. Conventiona…
Jammer classification with Federated Learning
Peng Wu, Helena Calatrava, Tales Imbiriba +1
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation. Given their ubiquity, jamming mitigation and localization techniques are of crucial impo…
Bayesian data fusion with shared priors
Peng Wu, Tales Imbiriba, Victor Elvira +1
The integration of data and knowledge from several sources is known as data fusion. When data is only available in a distributed fashion or when different sensors are used to infer…
Personalized Federated Learning over non-IID Data for Indoor Localization
Peng Wu, Tales Imbiriba, Junha Park +2
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models. In the…