An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective

Recently, federated multi-view clustering (FedMVC) has gained attention for its ability to mine complementary clustering structures from multiple clients without exposing private data. Existing methods mainly focus on addressing the feature heterogeneity problem brought by views on different clients and mitigating it using shared client information. Although these methods have achieved performance improvements, the information they choose to share, such as model parameters or intermediate outputs, inevitably raises privacy concerns. In this paper, we propose an Effective and Secure Federated Multi-view Clustering method, ESFMC, to alleviate the dilemma between privacy protection and performance improvement. This method leverages the information-theoretic perspective to split the features extracted locally by clients, retaining sensitive information locally and only sharing features that are highly relevant to the task. This can be viewed as a form of privacy-preserving information sharing, reducing privacy risks for clients while ensuring that the server can mine high-quality global clustering structures. Theoretical analysis and extensive experiments demonstrate that the proposed method more effectively mitigates the trade-off between privacy protection and performance improvement compared to state-of-the-art methods.

Imagine a group of hospitals, each with valuable patient data, trying to work together to discover patterns that could improve healthcare. However, they can’t share patient records directly because of privacy laws and ethical concerns. So how can they collaborate without exposing sensitive information?Our research offers a solution. We propose a new method that lets different organizations, like hospitals, banks, or schools, work together to find useful insights without sharing private data. Instead of sending raw data or detailed personal information, our method allows each participant to keep the private details safely on their own system. They only share carefully chosen pieces of information that are essential for the group task. This helps build a clearer overall picture while still respecting everyone’s privacy. What’s more, our method works even when some participants don’t have complete data. For example, there is only partial overlap between the patients of one hospital and those of another hospital. We've designed a way for the method to still make sense of the incomplete puzzle. Tests show our method is not only safer but also more effective than existing techniques. It’s a step forward in allowing different groups to collaborate safely and intelligently in the digital age.