Exploring the Vulnerabilities of Federated Learning: A Deep Dive into
  Gradient Inversion Attacks
Federated Learning (FL) has emerged as a promising privacy-preserving
collaborative model training paradigm without sharing raw data. However, recent
studies have revealed that private information can still be leaked through
shared gradient information and attacked by Gradient Inversion Attacks (GIA).
While many GIA methods have been proposed, a detailed analysis, evaluation, and
summary of these methods are still lacking. Although various survey papers
summarize existing privacy attacks in FL, few studies have conducted extensive
experiments to unveil the effectiveness of GIA and their associated limiting
factors in this context. To fill this gap, we first undertake a systematic
review of GIA and categorize existing methods into three types, i.e.,
optimization-based GIA (OP-GIA), generation-based GIA
(GEN-GIA), and analytics-based GIA (ANA-GIA). Then, we comprehensively
analyze and evaluate the three types of GIA in FL, providing insights into the
factors that influence their performance, practicality, and potential threats.
Our findings indicate that OP-GIA is the most practical attack setting despite
its unsatisfactory performance, while GEN-GIA has many dependencies and ANA-GIA
is easily detectable, making them both impractical. Finally, we offer a
three-stage defense pipeline to users when designing FL frameworks and
protocols for better privacy protection and share some future research
directions from the perspectives of attackers and defenders that we believe
should be pursued. We hope that our study can help researchers design more
robust FL frameworks to defend against these attacks.
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Pengxin Guo
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2025-03-17T08:44:42.855Z
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Pengxin Guo
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gpx333
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Runxi Wang
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2025-03-17T08:44:39.377Z
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Runxi Wang
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Rx-Wang
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Shuang Zeng
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2025-03-17T08:53:09.089Z
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Shuang Zeng
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stevezs
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Jinjing Zhu
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2025-03-17T08:53:22.627Z
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Zhu
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Jinjing08
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Haoning Jiang
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2025-03-17T08:44:36.769Z
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HaoningJiang
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haoning666
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Yanran Wang
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2025-03-17T08:53:29.620Z
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Yanran Wang
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yanranw1
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Yuyin Zhou
admin_assigned
2025-03-17T08:53:35.758Z
66c7fb4ce2c92fe5b132f314
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Yuyin Zhou
user
RitaCoding
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Feifei Wang
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2025-03-17T08:53:43.569Z
653d6970885338b011d283d8
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Feifei Wang
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feifeiwang
67d778325121a10e6fc650bb
Hui Xiong
67d778325121a10e6fc650bc
Liangqiong Qu
admin_assigned
2025-03-17T08:53:51.160Z
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Liangqiong Qu
user
Liangqiong-QU
Exploring the Vulnerabilities of Federated Learning: A Deep Dive into
  Gradient Inversion Attacks
Federated Learning (FL) has emerged as a promising privacy-preserving
collaborative model training paradigm without sharing raw data. However, recent
studies have revealed that private information can still be leaked through
shared gradient information and attacked by Gradient Inversion Attacks (GIA).
While many GIA methods have been proposed, a detailed analysis, evaluation, and
summary of these methods are still lacking. Although various survey papers
summarize existing privacy attacks in FL, few studies have conducted extensive
experiments to unveil the effectiveness of GIA and their associated limiting
factors in this context. To fill this gap, we first undertake a systematic
review of GIA and categorize existing methods into three types, i.e.,
optimization-based GIA (OP-GIA), generation-based GIA
(GEN-GIA), and analytics-based GIA (ANA-GIA). Then, we comprehensively
analyze and evaluate the three types of GIA in FL, providing insights into the
factors that influence their performance, practicality, and potential threats.
Our findings indicate that OP-GIA is the most practical attack setting despite
its unsatisfactory performance, while GEN-GIA has many dependencies and ANA-GIA
is easily detectable, making them both impractical. Finally, we offer a
three-stage defense pipeline to users when designing FL frameworks and
protocols for better privacy protection and share some future research
directions from the perspectives of attackers and defenders that we believe
should be pursued. We hope that our study can help researchers design more
robust FL frameworks to defend against these attacks.
A study analyzes various Gradient Inversion Attacks (GIA) in Federated Learning (FL), categorizing them and providing insights into their effectiveness and practicality, while suggesting a defense pipeline for privacy protection.
Federated Learning Gradient Inversion Attacks optimization-based GIA generation-based GIA analytics-based GIA