An Advanced Gradient Leakage Attack Against Duplicate Labels via Model Outputs Reconstruction

Federated learning (FL) is a prevalent distributed machine learning framework that allows multiple clients to train one model by uploading gradients without sharing data, enabling cooperative learning while preserving the training data privacy. Nevertheless, recent research has revealed that shared gradients can still expose clients' private training data. These attacks, however, often become ineffective in two practical scenarios: (1) gradients are computed on high-resolution data; (2) labels are duplicated within the attacked batch. In this work, we introduce an advancedGradientLeakageAttack againstDuplicate labels (GLAD), which can effectively recover high-resolution training data from gradients while considering duplicate labels, making it applicable in more realistic FL scenarios. The key technique ofGLADis to formalize the relationships between model outputs, gradients, model parameters, and training data labels. Based on these relationships,GLADfurther reconstructs the model outputs and inverts the reconstructed model outputs back to the corresponding model inputs. Our method can achieve state-of-the-art recovery accuracy while ensuring efficiency. Extensive experimental results demonstrate thatGLADcan reconstruct images of 224× 224pixels with a batch size of 256 with duplicate labels. Our source code is available athttps://github.com/SuperX612/GLAD.