Federated Fuzzy C-means with Schatten-p Norm Minimization

Wei Feng, Zhenwei Wu, Qianqian Wang, Bo Dong, Quanxue Gao

Abstract: Federated multi-view clustering aims to provide a feasible and effective solution for handling unlabeled data owned by multiple clients. There are two main challenges: 1) The local data is always sensitive, thus preventing any inadvertent data leakage to the server or other clients. 2) Multi-view data contain both consistency and complementarity information, necessitating thorough exploration and utilization of these aspects to achieve enhanced clustering performance. Fully considering the above challenges, in this paper, we propose a novel federated multi-view method named Federated Fuzzy C-Means with Schatten-p Norm Minimization(FFCMSP) which is based on Fuzzy C-Means and Schatten p-norm. Specifically, we utilize the membership degrees to replace conventional hard clustering assignment in K-means, enabling improved uncertainty handling and less information loss. Moreover, we introduce a Schatten p-norm-based regularizer to fully explore the inter-view complementary information and global spatial structure. We also develop a federated optimization algorithm enabling clients to collaboratively learn the clustering results. Extensive experiments on several datasets demonstrate that our proposed method exhibits superior performance in federated multi-view clustering.

Primary Subject Area: [Content] Multimodal Fusion

Relevance To Conference: In this paper, we propose a novel federated multi-view method named Federated Fuzzy C-Means with Schatten-p Norm Minimization(FFCMSP) which is based on Fuzzy C-Means and Schatten p-norm. Specifically, we utilize the membership degrees to replace conventional hard clustering results in K-means, enabling improved uncertainty handling and less information loss. Moreover, we introduce a Schatten p-norm-based regularizer to fully explore the inter-view complementary information and global spatial structure. Correspondingly, we also proposed a federated optimization algorithm enabling clients to collaboratively learn the clustering results.

Submission Number: 4668