The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has promoted a transformative shift in computing paradigms from centralized data processing to privacy-preserved distributed data processing. Federated analytics (FA) is an emerging technique to support collaborative data analytics among diverse data owners without centralizing the raw data. Despite the wide applications of FA in industry and academia, a comprehensive examination of existing research efforts in FA has been notably absent. This survey aims to bridge this gap by first providing an overview of FA, elucidating key concepts, and discussing its relationship with similar concepts. We then conduct a thorough examination of FA, including its taxonomy, key challenges, and enabling techniques. Diverse FA applications, including statistical metrics, set computation, frequency-related applications, database query operations, model-based applications, FL-assisting FA tasks, and other wireless network applications are then carefully reviewed. We complete the survey with several open research issues and future directions. This survey intends to provide a holistic understanding of the emerging FA techniques and foster the continued evolution of privacy-preserving distributed data processing in the emerging networked society.

翻译：联网边缘设备生成的数据量激增，加之对数据隐私意识的日益增强，推动了计算范式从集中式数据处理向隐私保护的分布式数据处理的变革性转变。联邦分析（FA）作为一种新兴技术，支持不同数据所有者在不集中原始数据的情况下进行协作数据分析。尽管FA在工业界和学术界得到广泛应用，但对其现有研究成果的全面梳理尚明显缺失。本综述旨在弥合这一空白，首先概述FA的基本概念，阐明关键术语，并讨论其与相似概念的关系。随后，我们对FA进行深入剖析，涵盖其分类体系、关键挑战及使能技术。接着，系统梳理了多样化的FA应用场景，包括统计指标、集合计算、频率相关应用、数据库查询操作、基于模型的应用、辅助联邦学习的FA任务及其他无线网络应用。最后，本文提出若干开放研究问题与未来方向。本综述旨在为新兴FA技术提供全面认知，并推动隐私保护的分布式数据处理在日益涌现的网络化社会中的持续演进。