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A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions

Authors:

Jiankun HuAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 6

Article No.: 131, Pages 1 - 36

https://doi.org/10.1145/3460427

Published: 13 July 2021 Publication History

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Abstract

The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

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ACM Computing Surveys Volume 54, Issue 6

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July 2022

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EISSN:1557-7341

DOI:10.1145/3475936

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