NeurIPS 2025

OmniFC: Rethinking Federated Clustering via
Lossless and Secure Distance Reconstruction
Jie Yan
Jing Liu
Zhong-Yuan Zhang∗
Central University of Finance and Economics
jieyan@email.cufe.edu.cn, liu_jing0623@163.com, zhyuanzh@gmail.com
Abstract
Federated clustering (FC) aims to discover global cluster structures across decentral-
ized clients without sharing raw data, making privacy preservation a fundamental
requirement. There are two critical challenges: (1) privacy leakage during collab-
oration, and (2) robustness degradation due to aggregation of proxy information
from non-independent and identically distributed (Non-IID) local data, leading to
inaccurate or inconsistent global clustering. Existing solutions typically rely on
model-specific local proxies, which are sensitive to data heterogeneity and inherit
inductive biases from their centralized counterparts, thus limiting robustness and
generality. We propose Omni Federated Clustering (OmniFC), a unified and model-
agnostic framework. Leveraging Lagrange coded computing, our method enables
clients to share only encoded data, allowing exact reconstruction of the global
distance matrix—a fundamental representation of sample relationships—without
leaking private information, even under client collusion. This construction is nat-
urally resilient to Non-IID data distributions. This approach decouples FC from
model-specific proxies, providing a unified extension mechanism applicable to
diverse centralized clustering methods. Theoretical analysis confirms both re-
construction fidelity and privacy guarantees, while comprehensive experiments
demonstrate OmniFC’s superior robustness, effectiveness, and generality across
various benchmarks compared to state-of-the-art methods. Code will be released.
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Introduction
Traditional clustering methods presuppose centralized access to the entire dataset, enabling the
construction of global structures such as cluster centroids or kernel matrices. However, in federated
settings ...