Federated Analytics: A Survey

被引：17

作者：

Elkordy, Ahmed Roushdy ^{[1
]}

Ezzeldin, Yahya H. ^{[1
]}

Han, Shanshan ^{[2
]}

Sharma, Shantanu ^{[3
]}

He, Chaoyang ^{[4
]}

Mehrotra, Sharad ^{[2
]}

Avestimehr, Salman ^{[1
]}

机构：

[1] Univ Southern Calif, Los Angeles, CA 90007 USA

[2] Univ Calif Irvine, Irvine, CA USA

[3] New Jersey Inst Technol, Newark, NJ 07102 USA

[4] FedML Inc, Sunnyvale, CA USA

来源：

APSIPA TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING | 2023年 / 12卷 / 01期

关键词：

Federated analytics; distributed computing; privacy; SET INTERSECTION; PRIVATE; SECURE; COMPLEXITY;

D O I：

10.1561/116.00000063

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：33

共 97 条

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