APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 1

Federated Analytics: A Survey

Ahmed Roushdy Elkordy, University of Southern California, USA, Yahya H. Ezzeldin, University of Southern California, USA, yessa@usc.edu , Shanshan Han, University of California, Irvine, USA, Shantanu Sharma, New Jersey Institute of Technology, USA, Chaoyang He, FedML Inc., USA, Sharad Mehrotra, University of California, Irvine, USA, Salman Avestimehr, University of Southern California, USA
 
Suggested Citation
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra and Salman Avestimehr (2023), "Federated Analytics: A Survey", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 1, e4. http://dx.doi.org/10.1561/116.00000063

Publication Date: 30 Jan 2023
© 2023 A. R. Elkordy, Y. H. Ezzeldin, S. Han, S. Sharma, C. He, S. Mehrotra and S. Avestimehr
 
Subjects
 
Keywords
Federated analyticsdistributed computingprivacy
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
What is Federated Analytics? 
A Taxonomy of Federated Analytics Queries 
Existing Solutions to Statistical Testing Queries 
Existing Solutions to Set Queries 
Existing Solutions to Matrix Transformation 
Challenges and Open Opportunities 
Conclusion 
References 

Abstract

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.

DOI:10.1561/116.00000063