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

Xinyue Chen, Jinfeng Peng, Yuhao Li, Xiaorong Pu, Yang Yang, Yazhou Ren
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:8871-8889, 2025.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-chen25az, title = {An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective}, author = {Chen, Xinyue and Peng, Jinfeng and Li, Yuhao and Pu, Xiaorong and Yang, Yang and Ren, Yazhou}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {8871--8889}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/chen25az/chen25az.pdf}, url = {https://proceedings.mlr.press/v267/chen25az.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective %A Xinyue Chen %A Jinfeng Peng %A Yuhao Li %A Xiaorong Pu %A Yang Yang %A Yazhou Ren %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-chen25az %I PMLR %P 8871--8889 %U https://proceedings.mlr.press/v267/chen25az.html %V 267 %X 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.
APA
Chen, X., Peng, J., Li, Y., Pu, X., Yang, Y. & Ren, Y.. (2025). An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:8871-8889 Available from https://proceedings.mlr.press/v267/chen25az.html.

Related Material