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

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Yin, Xuefei, Zhu, Yanming, Hu, Jiankun·2022·ACM Computing Surveys

DOI 10.1145/3460427

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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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Authors: Yin, X., Zhu, Y., & Hu, J.
Affiliations: UNSW Canberra (AU)
UNSW Sydney (AU)
ORCID: Yin, Xuefei
Zhu, Yanming
Hu, Jiankun
Venue: ACM Computing Surveys
Publisher: Association for Computing Machinery (ACM)
Published: 2021-07-13
Volume: 54
Issue: 6
Pages: 1-36
ISSN: 0360-0300 (ISSN-L)
1557-7341
DOI: 10.1145/3460427
OpenAlex: W3180608480
Cited by: 638
References: 133
Citations by year: 2026: 108 · 2025: 173 · 2024: 163 · 2023: 122 · 2022: 67 · 2021: 5
FWCI: 31.54
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Computing Surveys (publishedVersion)
Griffith Research Online (submittedVersion)
Funders: ARC (DP190103660;LP180100663)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
Keywords: privacy-preserving federated learning, federated learning, privacy leakage, data privacy, data utility, model aggregation, privacy-preserving machine learning, taxonomy (general classification scheme)

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

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