Diffusion to Obfuscation: Time-Adaptive Synthesized Generation Against Gradient Leakage Attacks in Federated Learning

Recent studies demonstrate that Federated Learning (FL) is vulnerable to gradient leakage attacks (GLAs). Revisiting key insights in GLAs under FL reveals that (1) Defenses should focus on protecting semantic and fine-grained details of data; (2) GLAs are effective mainly in early rounds; and (3) To avoid semantic leakage, defenses shouldn’t infer the true labels of private images for obfuscation. Building upon these insights, we present a simple, time-adaptive defense strategy that obfuscates the private gradient by employing the gradient from a synthesized image. To this end, a client trains a diffusion model on its own private dataset to generate synthesized images that fit the distribution of private dataset but are distinct in fine-grained details. In addition, a synthesized image is generated conditioned on a non-identical label from the private image to resist semantic leakage. Defense analysis and empirical evaluations demonstrate that our time-adaptive and label agnostic method can better maintain the trade-off between privacy preservation and model utility against GLAs. Our implementation is publicly available at https://github.com/lalakitchen/Diff2Obs.