Communication-Efficient Federated Multi-View Clustering
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a communication-efficient federated multi-view clustering method that reduces overhead by sharing pseudo-labels and centroids. The novel approach enhances privacy and efficiency in distributed machine learning.
Area Of Science
- Machine Learning
- Data Science
- Artificial Intelligence
Background
- Federated multi-view clustering (FMVC) enables privacy-preserving data grouping across distributed clients.
- Existing FMVC methods suffer from high communication overhead and insufficient utilization of data similarities for large-scale datasets.
Purpose Of The Study
- To propose a communication-efficient federated multi-view clustering framework.
- To address the limitations of existing methods regarding communication costs and data similarity utilization.
Main Methods
- Developed a framework approximating data representation using shared pseudo-labels and centroid matrices.
- Incorporated a linear kernel function to effectively consider pairwise data similarities without explicit computation.
- Achieved linear complexity concerning the number of samples for optimization.
Main Results
- Demonstrated significant improvements over existing federated multi-view clustering methods.
- Achieved an average accuracy improvement of 26.84% and up to 98.4% communication overhead reduction.
- Outperformed centralized multi-view clustering approaches in both performance and computational efficiency, with substantial speedups.
Conclusions
- The proposed communication-efficient federated multi-view clustering framework effectively reduces communication overhead and enhances computational efficiency.
- The method successfully leverages data similarities and achieves superior clustering performance compared to existing federated and centralized approaches.
- This framework offers a promising solution for large-scale, privacy-preserving multi-view clustering tasks.
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