# Evolving Fuzzy Clustering

Federated clustering previously revealed more model information to the server each round, and even without raw samples, repeated exchanges made client contributions linkable. eFDPC combines evolving fuzzy clustering with density peak clustering in a federated setting: each client evolves micro-clusters from private mini-batches and transmits only density-peak candidates as additive shares through a shuffler layer, so the server reconstructs peaks after mixing and selects global peaks. On synthetic and real benchmarks, it outperforms recent federated clustering baselines in one-shot settings and scales to higher-dimensional, non-IID data.

Federated clustering previously revealed more model information to the server each round, and even without raw samples, repeated exchanges made client contributions linkable. eFDPC combines evolving fuzzy clustering with density peak clustering in a federated setting: each client evolves micro-clusters from private mini-batches and transmits only density-peak candidates as additive shares through a shuffler layer, so the server reconstructs peaks after mixing and selects global peaks. On synthetic and real benchmarks, it outperforms recent federated clustering baselines in one-shot settings and scales to higher-dimensional, non-IID data.