  Federated Learning (FL) is a setting where multiple parties with distributed
data collaborate in training a joint Machine Learning (ML) model while keeping
all data local at the parties. Federated clustering is an area of research
within FL that is concerned with grouping together data that is globally
similar while keeping all data local. We describe how this area of research can
be of interest in itself, or how it helps addressing issues like
non-independently-identically-distributed (i.i.d.) data in supervised FL
frameworks. The focus of this work, however, is an extension of the federated
fuzzy $c$-means algorithm to the FL setting (FFCM) as a contribution towards
federated clustering. We propose two methods to calculate global cluster
centers and evaluate their behaviour through challenging numerical experiments.
We observe that one of the methods is able to identify good global clusters
even in challenging scenarios, but also acknowledge that many challenges remain
open.
