  The rapid growth of data from edge devices has catalyzed the performance of
machine learning algorithms. However, the data generated resides at client
devices thus there are majorly two challenge faced by traditional machine
learning paradigms - centralization of data for training and secondly for most
the generated data the class labels are missing and there is very poor
incentives to clients to manually label their data owing to high cost and lack
of expertise. To overcome these issues, there have been initial attempts to
handle unlabelled data in a privacy preserving distributed manner using
unsupervised federated data clustering. The goal is partition the data
available on clients into $k$ partitions (called clusters) without actual
exchange of data. Most of the existing algorithms are highly dependent on data
distribution patterns across clients or are computationally expensive.
Furthermore, due to presence of skewed nature of data across clients in most of
practical scenarios existing models might result in clients suffering high
clustering cost making them reluctant to participate in federated process. To
this, we are first to introduce the idea of personalization in federated
clustering. The goal is achieve balance between achieving lower clustering cost
and at same time achieving uniform cost across clients. We propose p-FClus that
addresses these goal in a single round of communication between server and
clients. We validate the efficacy of p-FClus against variety of federated
datasets showcasing it's data independence nature, applicability to any finite
$\ell$-norm, while simultaneously achieving lower cost and variance.
