Federated Analytics: A survey
February 02, 2023 ยท The Cartographer ยท ๐ APSIPA Transactions on Signal and Information Processing
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Authors
Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Shanshan Han, Shantanu Sharma, Chaoyang He, Sharad Mehrotra, Salman Avestimehr
arXiv ID
2302.01326
Category
cs.LG: Machine Learning
Cross-listed
cs.CR
Citations
36
Venue
APSIPA Transactions on Signal and Information Processing
Last Checked
2 days ago
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.
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