  Federated clustering (FC) is an extension of centralized clustering in
federated settings. The key here is how to construct a global similarity
measure without sharing private data, since the local similarity may be
insufficient to group local data correctly and the similarity of samples across
clients cannot be directly measured due to privacy constraints. Obviously, the
most straightforward way to analyze FC is to employ the methods extended from
centralized ones, such as K-means (KM) and fuzzy c-means (FCM). However, they
are vulnerable to non independent-and-identically-distributed (non-IID) data
among clients. To handle this, we propose a new federated clustering framework,
named synthetic data aided federated clustering (SDA-FC). It trains generative
adversarial network locally in each client and uploads the generated synthetic
data to the server, where KM or FCM is performed on the synthetic data. The
synthetic data can make the model immune to the non-IID problem and enable us
to capture the global similarity characteristics more effectively without
sharing private data. Comprehensive experiments reveals the advantages of
SDA-FC, including superior performance in addressing the non-IID problem and
the device failures.
