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应用科学学报 ›› 2026, Vol. 44 ›› Issue (5): 861-874.doi: 10.3969/j.issn.0255-8297.2026.05.012

• 人工智能技术与应用 • 上一篇    

一种面向簇分布异构的一次通信联邦聚类方法

马小锋1,2, 张诚1,2, 史汶泽3, 陆林1,2   

  1. 1. 三峡大学 水电工程智能视觉监测湖北省重点实验室, 湖北 宜昌 443002;
    2. 三峡大学 计算机与信息学院, 湖北 宜昌 443002;
    3. 扬州大学 广陵学院, 江苏 扬州 225000
  • 收稿日期:2025-10-29 发布日期:2026-10-09
  • 通信作者: 陆林，高级工程师，研究方向为驾驶行为、隐私计算。E-mail:lulin@ctgu.edu.cn
  • 基金资助:
    湖北省自然科学基金项目（No.2025AFB538）

One-Shot Federated Clustering Method for Cluster Distribution Heterogeneity

MA Xiaofeng1,2, ZHANG Cheng1,2, SHI Wenze3, LU Lin1,2   

  1. 1. Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, Hubei, China;
    2. College of Computer and Information Technology, China Three Gorges University, Yichang 443002, Hubei, China;
    3. Guangling College of Yangzhou University, Yangzhou 225000, Jiangsu, China
  • Received:2025-10-29 Published:2026-10-09

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摘要： 联邦聚类在非独立同分布场景下易受数据异构性影响，难以保持模型性能的鲁棒性。传统多轮迭代聚类方法通信开销大、隐私风险高，难以适用于资源受限环境。为此，本文提出一种面向簇分布异构的一次通信联邦聚类方法：联邦贝叶斯加权聚类（federatedBayesian weighted clustering,FedBWC）。该方法在客户端采用贝叶斯高斯混合模型对本地数据进行变分推断，自适应识别有效簇并提取聚类中心及置信度权重；在服务器端，仅需单轮通信执行加权K均值聚合，利用动态权重分配提升全局中心的代表性。基于6个基准数据集（Thyroid、Balance Scale、Digits、Waveform、USPS、PenDigits）的系统实验表明，在强异构场景（Dirichlet参数为0.3）下，FedBWC相较于对比方法在V-measure、ARI和AMI上平均提升分别为21.6%、28.7%和22.6%，在中度异构场景（Dirichlet参数为0.5）下亦保持超5%的性能优势，整体表现显著优于现有方法，为复杂异构环境下的联邦聚类提供了高效且鲁棒的解决方案。

关键词: 联邦聚类, 联邦学习, 数据异构, 隐私保护, 贝叶斯高斯混合

Abstract: Federated clustering is vulnerable to data heterogeneity under non-independent and non-identically distributed (Non-IID) conditions, making it difficult to maintain robust model performance. Traditional multi-round iterative clustering methods incur high communication overhead and privacy risks, making them unsuitable for resource-constrained environments. To address this issue, this paper proposes federated Bayesian weighted clustering (FedBWC), a one-shot federated clustering method designed to handle heterogeneity in cluster distributions. On the client side, a Bayesian Gaussian mixture model (BGM) with variational inference is applied to local data to adaptively identify effective clusters and extract their centers along with corresponding confidence weights. On the server side, weighted K-means aggregation is performed with only a single communication round, where dynamic weight assignment enhances the representativeness of global centers. Systematic experiments were conducted on six benchmark datasets (Thyroid, Balance Scale, Digits, Waveform, USPS, and PenDigits). The results show that under strong heterogeneity conditions (Dirichlet parameter = 0.3), FedBWC achieves average improvements of 21.6% in V-measure, 28.7% in ARI, and 22.6% in AMI over the baseline methods, while retaining a performance advantage of more than 5% under moderate heterogeneity conditions (Dirichlet parameter = 0.5). Overall, FedBWC significantly outperforms existing methods and provides an efficient and robust solution for federated clustering in complex heterogeneous environments.

Key words: federated clustering, federated learning, data heterogeneity, privacy protection, Bayesian Gaussian mixture

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