title: Exploring the Vulnerabilities of Federated Learning: A Deep Dive into
    Gradient Inversion Attacks
summary: 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.
paper_authors._id: 67d778325121a10e6fc650b3
paper_authors.name: Pengxin Guo
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paper_authors.name: Runxi Wang
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paper_authors.fullname: Runxi Wang
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paper_authors.user: Rx-Wang
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paper_authors.name: Shuang Zeng
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paper_authors.fullname: Shuang Zeng
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paper_authors.user: stevezs
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paper_authors.name: Jinjing Zhu
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paper_authors.fullname: Zhu
paper_authors.type: user
paper_authors.user: Jinjing08
paper_authors._id: 67d778325121a10e6fc650b7
paper_authors.name: Haoning Jiang
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paper_authors.fullname: HaoningJiang
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paper_authors.user: haoning666
paper_authors._id: 67d778325121a10e6fc650b8
paper_authors.name: Yanran Wang
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paper_authors.fullname: Yanran Wang
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paper_authors.user: yanranw1
paper_authors._id: 67d778325121a10e6fc650b9
paper_authors.name: Yuyin Zhou
paper_authors.status: admin_assigned
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paper_authors.fullname: Yuyin Zhou
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paper_authors.user: RitaCoding
paper_authors._id: 67d778325121a10e6fc650ba
paper_authors.name: Feifei Wang
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paper_authors.fullname: Feifei Wang
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paper_authors.user: feifeiwang
paper_authors._id: 67d778325121a10e6fc650bb
paper_authors.name: Hui Xiong
paper_authors._id: 67d778325121a10e6fc650bc
paper_authors.name: Liangqiong Qu
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paper_authors.avatarUrl: /avatars/f55b8c3c8100d6b6d65ba61abc4fb014.svg
paper_authors.fullname: Liangqiong Qu
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paper_authors.user: Liangqiong-QU
paper_title: Exploring the Vulnerabilities of Federated Learning: A Deep Dive into
    Gradient Inversion Attacks
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: Federated Learning Gradient Inversion Attacks optimization-based GIA generation-based GIA analytics-based GIA