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.
@article{arxiv.2302.01326,
title = {Federated Analytics: A survey},
author = {Ahmed Roushdy Elkordy and Yahya H. Ezzeldin and Shanshan Han and Shantanu Sharma and Chaoyang He and Sharad Mehrotra and Salman Avestimehr},
journal= {arXiv preprint arXiv:2302.01326},
year = {2023}
}
Comments
To appear in APSIPA Transactions on Signal and Information Processing, Volume 12, Issue 1