Federated Analytics: A survey
It addresses the problem of enabling data analytics while preserving privacy for entities like mobile devices or institutions, but it is incremental as it surveys existing work.
This survey discusses federated analytics as a privacy-preserving framework for computing data analytics across multiple remote parties without sharing data, exploring its characteristics, differences from federated learning, and various queries and solutions.
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.