Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks
Published in arXiv preprint arXiv:2503.11514, 2025
This paper investigates the security risks of gradient inversion attacks in federated learning frameworks and proposes countermeasures to enhance privacy protection in collaborative machine learning.
Recommended citation: Guo.P, Wang.R, Zeng.S, Zhu.J, Jiang.H, Wang.Y, Zhou.Y, Wang.F, Xiong.H, & Qu.L (2025). "Exploring the Vulnerabilities of Federated Learning: A Deep Dive into Gradient Inversion Attacks." arXiv preprint arXiv:2503.11514. https://arxiv.org/abs/2503.11514