Review of Deep Gradient Inversion Attacks and Defenses in Federated Learning

SUN Yu; YAN Yu; CUI Jian; XIONG Gaojian; LIU Jianhua

doi:10.11999/JEIT230541

Volume 46 Issue 2

Feb. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(2): 428-442

SUN Yu, YAN Yu, CUI Jian, XIONG Gaojian, LIU Jianhua. Review of Deep Gradient Inversion Attacks and Defenses in Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 428-442. doi: 10.11999/JEIT230541

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SUN Yu, YAN Yu, CUI Jian, XIONG Gaojian, LIU Jianhua. Review of Deep Gradient Inversion Attacks and Defenses in Federated Learning[J]. Journal of Electronics & Information Technology, 2024, 46(2): 428-442. doi: 10.11999/JEIT230541

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Review of Deep Gradient Inversion Attacks and Defenses in Federated Learning

doi: 10.11999/JEIT230541

1.
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
2.
Key Laboratory of Ministry of Industry and Information Technology for Cyberspace Security (Beihang University), Beijing 100191, China

Funds: The National Natural Science Foundation of China (32071775)

Received Date: 2023-06-01
Rev Recd Date: 2023-12-01

Available Online: 2023-12-23

Publish Date: 2024-02-29

Abstract

Abstract

As a distributed machine learning approach that preserves data ownership while releasing data usage rights, federated learning overcomes the challenge of data silos that hinder large-scale modeling with big data. However, the characteristic of only sharing gradients without training data during the federated training process does not guarantee the confidentiality of users’ training data. In recent years, novel deep gradient inversion attacks have demonstrated the ability of adversaries to reconstruct private training data from shared gradients, which poses a serious threat to the privacy of federated learning. With the evolution of gradient inversion techniques, adversaries are increasingly capable of reconstructing large volumes of data from deep neural networks, which challenges the Privacy-Preserving Federated Learning (PPFL) with encrypted gradients. Effective defenses mainly rely on perturbation transformations to obscure original gradients, inputs, or features to conceal sensitive information. Firstly, the gradient inversion vulnerability in PPFL is highlighted and the threat model in gradient inversion is presented. Then a detailed review of deep gradient inversion attacks is conducted from the perspectives of paradigms, capabilities, and targets. The perturbation-based defenses are divided into three categories according to the perturbed objects: gradient perturbation, input perturbation, and feature perturbation. The representative works in each category are analyzed in detail. Finally, an outlook on future research directions is provided.
- Federated learning,
- Gradient inversion,
- Data reconstruction,
- Label restoration,
- Perturbation transformation

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References(71)

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