Practical Feasibility of Gradient Inversion Attacks in Federated Learning

Excerpt

arXiv:2508.19819v3 Announce Type: replace-cross Abstract: Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable experimental settings. However, it remains unclear whether such attacks are feasible in modern, performance-optimized systems deployed in practice. In this work, we evaluate the practical feasibility of gradient inversion for image-based federated learning. We cond