摘要：

在大数据时代, 存在大量多视图数据, 现有的多视图聚类方法大都把所有视图数据汇总到一起进行学习, 但在实际应用中, 不同视图的数据大多存储在不同的设备中, 甚至有些设备上的数据涉及隐私, 无法共享。如果把每个视图的数据视为分布式网络中的一个节点, 联邦学习则可有效解决数据无法共享和隐私保护的问题, 联邦多视图聚类正是将联邦学习引入多视图聚类而得到的一类方法。联邦学习利用中心服务器进行协调, 当中心服务器缺失或出现故障时, 该方法将失效。为此, 提出一种去中心的联邦多视图聚类(DFMC)方法。首先通过非负矩阵分解(NMF)学习每个视图的低维表示, 然后根据视图信息的一致性, 针对不同视图的低维表示给出一致性约束, 该约束可以实现邻居视图间的通信, 构建去中心的联邦学习环境, 得到一个统一的低维表示, 进而进行聚类。在此基础上, 使用交替极小化(AM)算法对每个视图分别进行求解, 从而实现隐私保护。在真实数据集上的实验结果验证了DFMC的有效性和收敛性。

关键词: 多视图聚类, 非负矩阵分解, 联邦学习, 去中心, 隐私保护

Abstract:

In the era of big data, multi-view data exist in large quantities, and most existing multi-view clustering methods aggregate the data of all views for learning. However, data from different views are stored on different devices in some practical applications, and some of the data are private and cannot be shared. If the data of each view are regarded as nodes in a distributed network, these problems can be solved by introducing federated learning into multi-view clustering. Federated learning utilizes a central server for coordination; however, it becomes invalid when the central server is missing or faulty. This paper proposes a Decentralized Federated Multi-view Clustering (DFMC) approach to address this issue. First, the low-dimensional representation of each view is learned using Non-negative Matrix Factorization (NMF). Next, a consistency constraint is applied to the low-dimensional representations of different views based on the consistency of the view information. This constraint implements information communication between neighboring views and constructs a decentralized federated learning environment. Finally, a unified low-dimensional representation matrix is obtained and applied for clustering. Privacy preservation is achieved using the Alternating Minimization (AM) algorithm for individual views separately. Experimental results on real datasets verify the effectiveness and convergence of the DFMC approach.

Key words: multi-view clustering, Non-negative Matrix Factorization (NMF), federated learning, decentralization, privacy protection