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.

翻译：联邦重击者分析涉及在分布式数据中识别出现频率最高的项。现有方法在此任务中常面临隐私泄露或效用损失等挑战。为解决这些问题，我们提出了一种新颖的隐私保护算法，通过利用分层结构，借助扰动与相似性技术，在非独立同分布数据中发现局部和全局重击者。我们在合成数据集和真实数据集上进行了广泛评估，以验证我们方法的有效性。此外，我们推出了FedCampus这一演示应用，以展示我们的算法在分析人口统计方面的能力。