Federated learning has been widely studied in recent years, which acts to avoid the privacy leakage problem while ensuring co-learning among clients. Most existing federated clustering methods are mainly focusing on extending the learning strategy of k-means to a federated scenario. This unavoidably makes the clustering inherit the inherent limitations brought by k-means, i.e., each learning client requires the number of true clusters k to be given in advance, which is not always the case in reality. Moreover, the “uniform effect” that prevents the existing clustering methods from effectively partitioning imbalanced clusters also remains unsolved for federated clustering. It is worth noting that a too-small k also contributes to the “uniform effect” as the granularity that will be searched by the clustering algorithm is fixed to be relatively large. To solve the above problems, we propose the Federated Hierarchical Clustering (FedHC) method to explore subclusters at each client and then merge them at the server to form a sought number of clusters. Such a process simultaneously protects privacy and aggregates the cluster distribution information that is finely learned by each client without requiring a pre-set “true” local k. Since we do not force each client to search for the same and small number of clusters, they can provide rich micro-cluster distribution information to the server, and the server hierarchically merges closely distributed subclusters to avoid the “uniform effect”. It turns out that FedHC can effectively explore imbalanced clusters without passing the privacy information. Several experiments have been conducted to illustrate the efficacy of FedHC.