  In the resource management of wireless networks, Federated Learning has been
used to predict handovers. However, non-independent and identically distributed
data degrade the accuracy performance of such predictions. To overcome the
problem, Federated Learning can leverage data clustering algorithms and build a
machine learning model for each cluster. However, traditional data clustering
algorithms, when applied to the handover prediction, exhibit three main
limitations: the risk of data privacy breach, the fixed shape of clusters, and
the non-adaptive number of clusters. To overcome these limitations, in this
paper, we propose a three-phased data clustering algorithm, namely: generative
adversarial network-based clustering, cluster calibration, and cluster
division. We show that the generative adversarial network-based clustering
preserves privacy. The cluster calibration deals with dynamic environments by
modifying clusters. Moreover, the divisive clustering explores the different
number of clusters by repeatedly selecting and dividing a cluster into multiple
clusters. A baseline algorithm and our algorithm are tested on a time series
forecasting task. We show that our algorithm improves the performance of
forecasting models, including cellular network handover, by 43%.
