Federated K-means Clustering
This work provides a practical solution for privacy-preserving unsupervised learning in distributed data settings, though it is incremental as it adapts an existing method to a federated context.
The paper tackled the scarcity of unsupervised federated learning methods by introducing a federated K-means clustering algorithm that addresses challenges like varying cluster numbers across centers and convergence on less separable datasets, achieving competitive performance on benchmark datasets.
Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce. This work introduces an algorithm which implements K-means clustering in a federated manner, addressing the challenges of varying number of clusters between centers, as well as convergence on less separable datasets.