Federated Analytics: A Survey

Authors

Ahmed Roushdy Elkordy, University of Southern California
Yahya H. Ezzeldin, University of Southern California
Shanshan Han, University of California, Irvine
Shantanu Sharma, New Jersey Institute of Technology
Chaoyang He, FedML Inc.
Sharad Mehrotra, University of California, Irvine
Salman Avestimehr, University of Southern California

Document Type

Article

Publication Date

1-1-2023

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.

Identifier

85150986346 (Scopus)

Publication Title

Apsipa Transactions on Signal and Information Processing

External Full Text Location

https://doi.org/10.1561/116.00000063

e-ISSN

20487703

Issue

1

Volume

12

Grant

HR001120C0160

Fund Ref

Intel Corporation

Recommended Citation

Elkordy, Ahmed Roushdy; Ezzeldin, Yahya H.; Han, Shanshan; Sharma, Shantanu; He, Chaoyang; Mehrotra, Sharad; and Avestimehr, Salman, "Federated Analytics: A Survey" (2023). Faculty Publications. 2245.
https://digitalcommons.njit.edu/fac_pubs/2245