3 papers
cs.LG2026
One-Shot Federated Clustering of Non-Independent Completely Distributed Data
Yiqun Zhang, Shenghong Cai, Zihua Yang +3
Federated Learning (FL) that extracts data knowledge while protecting the privacy of multiple clients has achieved remarkable results in distributed privacy-preserving IoT systems,…
cs.LG2026
Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning
Shenghong Cai, Yiqun Zhang, Xiaopeng Luo +3
Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, th…
cs.LG2026
Stitch the Fragments: One-Shot Hierarchical Federated Clustering
Shenghong Cai, Zihua Yang, Yang Lu +4
Federated Clustering (FC) faces a critical bottleneck in real-world scenarios, i.e., global clusters are rarely intact, often fragmenting into incomplete, multi-granular unlabeled…