  Clustering clients into groups that exhibit relatively homogeneous data
distributions represents one of the major means of improving the performance of
federated learning (FL) in non-independent and identically distributed
(non-IID) data settings. Yet, the applicability of current state-of-the-art
approaches remains limited as these approaches cluster clients based on
information, such as the evolution of local model parameters, that is only
obtainable through actual on-client training. On the other hand, there is a
need to make FL models available to clients who are not able to perform the
training themselves, as they do not have the processing capabilities required
for training, or simply want to use the model without participating in the
training. Furthermore, the existing alternative approaches that avert the
training still require that individual clients have a sufficient amount of
labeled data upon which the clustering is based, essentially assuming that each
client is a data annotator. In this paper, we present REPA, an approach to
client clustering in non-IID FL settings that requires neither training nor
labeled data collection. REPA uses a novel supervised autoencoder-based method
to create embeddings that profile a client's underlying data-generating
processes without exposing the data to the server and without requiring local
training. Our experimental analysis over three different datasets demonstrates
that REPA delivers state-of-the-art model performance while expanding the
applicability of cluster-based FL to previously uncovered use cases.
