Gradient leakage attacks pose significant privacy risks in federated learning by exploiting transmitted gradients to reconstruct sensitive data. While existing defense mechanisms typically apply uniform perturbation across all gradients, we identify a critical oversight: privacy information in gradients exhibits inherent layer-wise heterogeneity. Through systematic analysis, we establish that different neural network layers contain varying amounts of reconstructable private information due to differential accumulation of nonlinear effects during gradient formation. This fundamental discovery enables our key innovation—Layer-Specific Gradient Protection (LSGP)—which pioneers surgical defense mechanisms that adapt protection intensity to each layer’s inherent privacy exposure level. Experimental results validate that LSGP achieves superior defense efficacy with comparable model utility compared to uniform protection baselines, establishing a new paradigm for efficient privacy-preserving machine learning through principled vulnerability analysis of gradient formation mechanics.