Search Results for author:
Found 6 papers, 2 papers with code
CCFC++: Enhancing Federated Clustering through Feature Decorrelation
In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data.
CCFC: Bridging Federated Clustering and Contrastive Learning
Benefiting from representation learning, the clustering performance of CCFC even double those of the best baseline methods in some cases.
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models
In the E-step, we aim to derive a mixture of Gaussian priors for the subsequent M-step.
Privacy-Preserving Federated Deep Clustering based on GAN
Federated clustering (FC) is an essential extension of centralized clustering designed for the federated setting, wherein the challenge lies in constructing a global similarity measure without the need to share private data.
Federated clustering with GAN-based data synthesis
Federated clustering (FC) is an extension of centralized clustering in federated settings.
Selective clustering ensemble based on kappa and F-score
Clustering ensemble has an impressive performance in improving the accuracy and robustness of partition results and has received much attention in recent years.
