ENHANCEMENT OF OBJECTIVE FUNCTION IN IMAGE RECOVERY ATTACKS UNDER GRADIENT COMPRESSION CONDITIONS IN FEDERATED LEARNING

Abstract

Image recovery attacks pose a significant privacy threat in distributed machine learning systems, even when gradient compression is employed. These attacks exploit gradient information to reconstruct original training data, raising serious concerns about data confidentiality. This study presents an improved method based on DLG to enhance image recovery accuracy under compressed gradient conditions. The proposed method introduces gradient masking to selectively retain significant gradient components and features a key innovation in the integration of Total Variation and L6-norm regularization terms to enhance image smoothness and mitigate artifacts. Experimental evaluations on MNIST and CIFAR-100 datasets reveal that the improved method significantly outperforms traditional DLG and HCGLA methods, particularly under extreme compression rates. By reducing visual distortions while preserving structural details, the proposed method provides valuable insights for enhancing data security in distributed learning and developing robust defenses against gradient compression attacks.