解决边缘设备非独立完全分布数据的聚类难题，提升联邦学习性能。
One-Shot Federated Clustering of Non-Independent Completely Distributed Data
- 提出GOLD框架，分三步：局部分布挖掘、全局融合、本地增强。
- 在多个数据集上聚类准确率提升12.3%~18.7%，显著优于现有方法。
- 适合处理无标签、非独立完全分布的物联网边缘聚类场景。
联邦学习在保护多客户端隐私的同时提取数据知识，在智能交通流监控、智能电网负载均衡等分布式隐私保护物联网系统中取得显著成果。由于多数边缘设备采集的数据无标签，无监督联邦聚类（FC）正日益成为探索复杂分布式数据模式知识的重要手段。然而，缺乏标签引导下，客户端普遍存在的非独立同分布（Non-IID）问题极大挑战了FC，主要表现为：如何融合来自非独立同分布客户端的模式知识（即聚类分布）；各客户端间的聚类分布如何关联；该关联如何影响全局知识融合。本文揭示了一个更棘手但被忽视的现象：不同客户端可能将同一聚类碎片化，由此引出更通用的非独立完全分布（Non-ICD）概念。为应对上述挑战，提出全新框架GOLD（Global Oriented Local Distribution Learning）。GOLD首先精细挖掘客户端潜在的不完整局部聚类分布，将其分布摘要上传至服务器进行全局融合，最后在全局分布指导下完成本地聚类增强。大量实验包括显著性检验、消融研究、可扩展性评估及定性结果，均验证了GOLD的优越性。
原文摘要 · Abstract (English)
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.
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