Federated Learning (FL) is a pivotal approach in decentralized machine learning, especially when data privacy is crucial and direct data sharing is impractical. While FL is typically associated with supervised learning, its potential in unsupervised scenarios is underexplored. This paper introduces a novel unsupervised federated learning methodology designed to identify the complete set of categories (global K) across multiple clients within label-free, non-uniform data distributions, a process known as Federated Clustering. Our approach, Federated Cluster-Wise Refinement (FedCRef), involves clients that collaboratively train models on clusters with similar data distributions. Initially, clients with diverse local data distributions (local K) train models on their clusters to generate compressed data representations. These local models are then shared across the network, enabling clients to compare them through reconstruction error analysis, leading to the formation of federated groups.In these groups, clients collaboratively train a shared model representing each data distribution, while continuously refining their local clusters to enhance data association accuracy. This iterative process allows our system to identify all potential data distributions across the network and develop robust representation models for each. To validate our approach, we compare it with traditional centralized methods, establishing a performance baseline and showcasing the advantages of our distributed solution. We also conduct experiments on the EMNIST and KMNIST datasets, demonstrating FedCRef's ability to refine and align cluster models with actual data distributions, significantly improving data representation precision in unsupervised federated settings.

本研究解决了联邦学习在无监督场景下的应用不足，提出了一种新颖的联邦聚类方法，实现无标签和不均匀数据分布下跨多个客户端的完整类别识别。通过设计联邦聚类逐步精炼方法(FedCRef)，该方法显示出在无监督的联邦学习环境中显著提高数据表示精度的潜力。

联邦聚类：针对去中心化数据分布的无监督聚类训练