Practical Feasibility of Gradient Inversion Attacks in Federated Learning
This study tested whether gradient inversion attacks remain practical against modern, high-performance vision models used in federated learning. The research found that contemporary architectures consistently resist meaningful image reconstruction, even under favorable attacker conditions. This suggests that high-fidelity visual data leakage is not a critical privacy risk in production systems.