One-Shot Federated Clustering of Non-Independent Completely Distributed Data

2026-01-24

IEEE Internet of Things Journal

AI Summary

This work addresses the performance bottleneck in unsupervised federated clustering caused by data fragmentation among non-independent and identically distributed (Non-IID) clients, and introduces a more general challenge termed “non-independent completely distributed” (Non-ICD). To tackle this, the authors propose GOLD, the first single-round communication federated clustering framework, which models incomplete local cluster distributions, uploads lightweight distribution summaries to the server, fuses them into a global distribution, and uses this global view to guide local clustering refinement. Experimental results demonstrate that GOLD significantly outperforms existing methods across diverse Non-ICD settings, while maintaining high efficiency, scalability, and strong privacy preservation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Federated Learning (FL) that extracts data knowledge while protecting the privacy of multiple clients has achieved remarkable results in distributed privacy-preserving IoT systems, including smart traffic flow monitoring, smart grid load balancing, and so on. Since most data collected from edge devices are unlabeled, unsupervised Federated Clustering (FC) is becoming increasingly popular for exploring pattern knowledge from complex distributed data. However, due to the lack of label guidance, the common Non-Independent and Identically Distributed (Non-IID) issue of clients have greatly challenged FC by posing the following problems: How to fuse pattern knowledge (i.e., cluster distribution) from Non-IID clients; How are the cluster distributions among clients related; and How does this relationship connect with the global knowledge fusion? In this paper, a more tricky but overlooked phenomenon in Non-IID is revealed, which bottlenecks the clustering performance of the existing FC approaches. That is, different clients could fragment a cluster, and accordingly, a more generalized Non-IID concept, i.e., Non-ICD (Non-Independent Completely Distributed), is derived. To tackle the above FC challenges, a new framework named GOLD (Global Oriented Local Distribution Learning) is proposed. GOLD first finely explores the potential incomplete local cluster distributions of clients, then uploads the distribution summarization to the server for global fusion, and finally performs local cluster enhancement under the guidance of the global distribution. Extensive experiments, including significance tests, ablation studies, scalability evaluations, qualitative results, etc., have been conducted to show the superiority of GOLD.

Problem

• Federated Clustering
• Non-IID
• Non-ICD
• Unsupervised Learning
• Data Heterogeneity

Innovation

• Federated Clustering
• Non-ICD
• GOLD
• One-Shot Learning
• Unsupervised Federated Learning