Exploring the Vulnerabilities of Federated Learning: A Deep Dive into
  Gradient Inversion Attacks
Authors: name: Pengxin Guo, name: Runxi Wang, name: Shuang Zeng, name: Jinjing Zhu, name: Haoning Jiang, name: Yanran Wang, name: Yuyin Zhou, name: Feifei Wang, name: Hui Xiong, name: Liangqiong Qu

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.