  Federated Learning is machine learning in the context of a network of clients
whilst maintaining data residency and/or privacy constraints. Community
detection is the unsupervised discovery of clusters of nodes within
graph-structured data. The intersection of these two fields uncovers much
opportunity, but also challenge. For example, it adds complexity due to missing
connectivity information between privately held graphs. In this work, we
explore the potential of federated community detection by conducting initial
experiments across a range of existing datasets that showcase the gap in
performance introduced by the distributed data. We demonstrate that isolated
models would benefit from collaboration establishing a framework for
investigating challenges within this domain. The intricacies of these research
frontiers are discussed alongside proposed solutions to these issues.
