Gradient Inversion Transcript: Leveraging Robust Generative Priors to Reconstruct Training Data from Gradient Leakage

2025-05-26

AI Summary

This paper addresses the training data reconstruction problem in distributed learning induced by gradient leakage. To tackle challenges including high gradient noise, data distribution shift, and model parameter heterogeneity, we propose GIT—a generative inversion attack model. GIT introduces a novel gradient-structure-aligned generative prior, explicitly embedding gradient geometric properties into the generator architecture to enable theory-guided, end-to-end reconstruction. The model supports offline training and plug-and-play deployment, serving as a universal prior that accelerates convergence and improves reconstruction fidelity across diverse optimization-based inversion methods. Evaluated on multiple benchmark datasets, GIT consistently outperforms state-of-the-art approaches under varying gradient error magnitudes, distribution shifts, and parameter heterogeneity, demonstrating superior robustness. Our work establishes an efficient and reliable new paradigm for assessing gradient privacy risks in distributed learning systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

We propose Gradient Inversion Transcript (GIT), a novel generative approach for reconstructing training data from leaked gradients. GIT employs a generative attack model, whose architecture is tailored to align with the structure of the leaked model based on theoretical analysis. Once trained offline, GIT can be deployed efficiently and only relies on the leaked gradients to reconstruct the input data, rendering it applicable under various distributed learning environments. When used as a prior for other iterative optimization-based methods, GIT not only accelerates convergence but also enhances the overall reconstruction quality. GIT consistently outperforms existing methods across multiple datasets and demonstrates strong robustness under challenging conditions, including inaccurate gradients, data distribution shifts and discrepancies in model parameters.

Problem

• Reconstruct training data from leaked gradients using generative approach
• Enhance reconstruction quality and convergence in distributed learning environments
• Improve robustness against inaccurate gradients and data distribution shifts

Innovation

• Generative attack model aligns with leaked structure
• Offline trained GIT relies solely on gradients
• Enhances convergence and reconstruction quality