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.

Federated Analytics is the latest feature added to Raven Distribution Framework that allows for the safe dynamic aggregation of statistics such as mean, variance, and standard deviation across data that is privately held on several clients. RDF's Ravop library now supports the creation of federated operations which developers can leverage to conduct analysis without directly observing a client's private data. Federated analytics is a new approach to data analysis in which key statistics like mean, variance, and standard deviation can be calculated across various private datasets without compromising privacy. It operates similarly to federated learning in that it runs local calculations over each client device's data and only makes the aggregated findings -- never any data from a specific device -- available to developers. Sensitive data like medical records, financial transactions, employee data, and others can be analyzed without leaving the premise.