Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.

翻译：联邦分析（FA）是一种隐私保护框架，用于在多方（如移动设备）或隔离的机构实体（如医院、银行）之间计算数据分析，而无需在各方之间共享数据。受联邦分析实际应用场景的启发，本文对联邦分析进行了系统性的讨论。具体而言，我们探讨了联邦分析的独特特性及其与联邦学习的区别。同时，我们广泛考察了多种FA查询类型，并讨论了针对不同FA查询的现有解决方案及潜在应用场景。