Federated K-means Clustering

About

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.

Swier Garst, Marcel Reinders · 2023

Related benchmarks

Task	Dataset	Result
Image Clustering	CIFAR-10	NMI—	318
Clustering	FMNIST	ARI44.9	43
Clustering	MNIST	ARI0.64	19
Clustering	EMNIST	ARI28.5	17
Clustering	20News	Accuracy (ACC)30.35	10
Clustering	Keck	ACC23.89	10
Clustering	CORe50	ACC25.04	10
Clustering	BBC News	Accuracy22.96	10
Clustering	Reuters Out-of-Sample	Accuracy61.74	9
Clustering	WebKB4 In-Sample	Accuracy54.54	9