联邦学习下保护隐私的深层子空间聚类方法
Federated Deep Subspace Clustering
- 客户端用编码-自表达-解码结构实现本地聚类
- 通过联邦聚合提升特征表达，聚类准确率显著提高
- 适合分布式数据场景下的隐私保护聚类任务
本文提出一种基于联邦学习框架的私密保护子空间聚类方法FDSC。每个客户端部署一个包含编码网络、自表达层和解码网络的深度子空间聚类网络，用于对本地数据进行分组。通过上传编码网络参数实现客户端间的协作，同时在本地保留数据邻域关系。联邦学习与局部结构保持的协同作用提升了编码器学习到的数据特征，增强了自表达学习能力，从而获得更优的聚类性能。在多个公开数据集上的实验表明，该方法优于现有聚类方法。
原文摘要 · Abstract (English)
This paper introduces FDSC, a private-protected subspace clustering (SC) approach with federated learning (FC) schema. In each client, there is a deep subspace clustering network accounting for grouping the isolated data, composed of a encode network, a self-expressive layer, and a decode network. FDSC is achieved by uploading the encode network to communicate with other clients in the server. Besides, FDSC is also enhanced by preserving the local neighborhood relationship in each client. With the effects of federated learning and locality preservation, the learned data features from the encoder are boosted so as to enhance the self-expressiveness learning and result in better clustering performance. Experiments test FDSC on public datasets and compare with other clustering methods, demonstrating the effectiveness of FDSC.
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