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:联邦分析师(FA)是一个隐私保护框架,用于在多方边远方(如移动设备)或集市机构实体(如医院、银行)之间不共享数据的情况下,对数据分析进行计算,而不在各方之间共享数据。受联邦分析师实际使用案例的驱使,我们关注对本篇文章中联邦分析师的系统讨论。特别是,我们讨论联邦分析师的独特性及其与联邦学习有何不同。我们还探讨广泛的联邦分析师询问,讨论各种现有解决方案以及不同联邦分析师的可能的运用案例应用。