Privacy preservation in federated learning: An insightful survey from the GDPR perspective
Open accessAbstract
In recent years, along with the blooming of Machine Learning (ML)-based applications and services, ensuring data privacy and security have become a critical obligation. ML-based service providers not only confront with difficulties in collecting and managing data across heterogeneous sources but also challenges of complying with rigorous data protection regulations such as EU/UK General Data Protection Regulation (GDPR). Furthermore, conventional centralised ML approaches have always come with long-standing privacy risks to personal data leakage, misuse, and abuse. Federated learning (FL) has emerged as a prospective solution that facilitates distributed collaborative learning without disclosing original training data. Unfortunately, retaining data and computation on-device as in FL are not sufficient for privacy-guarantee because model parameters exchanged among participants conceal sensitive information that can be exploited in privacy attacks. Consequently, FL-based systems are not naturally compliant with the GDPR. This article is dedicated to surveying of state-of-the-art privacy-preservation techniques in FL in relations with GDPR requirements. Furthermore, insights into the existing challenges are examined along with the prospective approaches following the GDPR regulatory guidelines that FL-based systems shall implement to fully comply with the GDPR. © 2021
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Existing one-shot FL methods are mostly impractical or face inherent limitations, such as requiring a public dataset, homogeneous participant models, additional data/model information upload, or unsatisfactory performance.
“Despite the low communication cost, existing one-shot FL methods are mostly impractical or face inherent limitations, e.g. , a public dataset is required, participants' models are homogeneous, additional data/model information needs to be uploaded, unsatisfactory performance [188], [189], [190].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [190]
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“Besides, in the current literature, privacy-preserving meth-ods in [31], [32], [33], and [34] illustrate that federated learning offers an alternative approach for integrating privacy in computer vision systems.”
Background
- Authors
- Truong, N., Sun, K., Wang, S., Guitton, F., & Guo, Y.
- Affiliations
- Imperial College London (GB)
- Hong Kong Baptist University (HK)
- Corresponding author
- Truong, Nguyen
- Guo, YiKe
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 110
- Pages
- 102402
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W3185987447
- Semantic Scholar
- f3c55aa04f67ee63c70ae2e956b44d7fbc14277e
- Cited by
- 399
- References
- 159
- Citations by year
- 2026: 73 · 2025: 118 · 2024: 83 · 2023: 69 · 2022: 44 · 2021: 12
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computers & Security (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Imperial College London
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
- Keywords
- federated learning, privacy preservation, GDPR compliance, data protection regulation, privacy attacks, personal data protection, regulatory compliance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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