  Federated heavy-hitter analytics involves the identification of the most
frequent items within distributed data. Existing methods for this task often
encounter challenges such as compromising privacy or sacrificing utility. To
address these issues, we introduce a novel privacy-preserving algorithm that
exploits the hierarchical structure to discover local and global heavy hitters
in non-IID data by utilizing perturbation and similarity techniques. We conduct
extensive evaluations on both synthetic and real datasets to validate the
effectiveness of our approach. We also present FedCampus, a demonstration
application to showcase the capabilities of our algorithm in analyzing
population statistics.
