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A Comprehensive Survey of Privacy-preserving Federated Learning: A Taxonomy, Review, and Future Directions
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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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“Furthermore, FL is a key PPML technique and widely studied, as reviewed in [167], [12], [46] on its taxonomy, advancements, and security challenges.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [167]
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Metadata
- Authors
- Yin, X., Zhu, Y., & Hu, J.
- Affiliations
- UNSW Canberra (AU)
- UNSW Sydney (AU)
- Venue
- ACM Computing Surveys
- Published
- 2021-07-13
- Volume
- 54
- Issue
- 6
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- 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
- 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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