4 citations · 7 across the 6 of their papers we have counts for
7 papers · 1 filter
OmniFC: Rethinking Federated Clustering via Lossless and Secure Distance Reconstruction
Jie Yan, Jing Liu, Zhong-Yuan Zhang
Federated clustering (FC) aims to discover global cluster structures across decentralized clients without sharing raw data, making privacy preservation a fundamental requirement. T…
CCFC++: Enhancing Federated Clustering through Feature Decorrelation
Jie Yan, Jing Liu, Yi-Zi Ning +1
In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data. This field has seen notable advancements through its marriage with co…
CCFC: Bridging Federated Clustering and Contrastive Learning
Jing Liu, Jie Yan, Zhong-Yuan Zhang
Federated clustering, an essential extension of centralized clustering for federated scenarios, enables multiple data-holding clients to collaboratively group data while keeping th…
ClusterDDPM: An EM clustering framework with Denoising Diffusion Probabilistic Models
Jie Yan, Jing Liu, Zhong-yuan Zhang
Variational autoencoder (VAE) and generative adversarial networks (GAN) have found widespread applications in clustering and have achieved significant success. However, the potenti…
Privacy-Preserving Federated Deep Clustering based on GAN
Jie Yan, Jing Liu, Ji Qi +1
Federated clustering (FC) is an essential extension of centralized clustering designed for the federated setting, wherein the challenge lies in constructing a global similarity mea…
Federated clustering with GAN-based data synthesis
Jie Yan, Jing Liu, Ji Qi +1
Federated clustering (FC) is an extension of centralized clustering in federated settings. The key here is how to construct a global similarity measure without sharing private data…