R-CONV++: Uncovering Privacy Vulnerabilities through Analytical Gradient Inversion Attacks

2025-09-23

AI Summary

Gradient inversion attacks in federated learning suffer significant performance degradation on convolutional layers, high-dimensional inputs, and small-batch settings. Method: We propose three analytical gradient inversion algorithms specifically designed for CNNs, enabling direct input reconstruction from gradients without recovering intermediate features—overcoming the non-invertibility of activation functions (e.g., ReLU) and supporting both single-sample and mini-batch reconstruction. Our approach integrates closed-form mathematical derivations with gradient constraint optimization to build an efficient, architecture-aware inversion framework. Contribution/Results: The method drastically reduces the number of required gradient constraints while achieving high-fidelity image reconstruction. Evaluated on diverse CNN architectures and real-world datasets (CIFAR-10, ImageNet subsets), it reconstructs original images with <5% of the gradient constraints needed by prior methods, significantly outperforming state-of-the-art approaches in fidelity and robustness across convolutional, high-dimensional, and low-batch regimes.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Non-convex Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Federated learning has emerged as a prominent privacy-preserving technique for leveraging large-scale distributed datasets by sharing gradients instead of raw data. However, recent studies indicate that private training data can still be exposed through gradient inversion attacks. While earlier analytical methods have demonstrated success in reconstructing input data from fully connected layers, their effectiveness significantly diminishes when applied to convolutional layers, high-dimensional inputs, and scenarios involving multiple training examples. This paper extends our previous work cite{eltaras2024r} and proposes three advanced algorithms to broaden the applicability of gradient inversion attacks. The first algorithm presents a novel data leakage method that efficiently exploits convolutional layer gradients, demonstrating that even with non-fully invertible activation functions, such as ReLU, training samples can be analytically reconstructed directly from gradients without the need to reconstruct intermediate layer outputs. Building on this foundation, the second algorithm extends this analytical approach to support high-dimensional input data, substantially enhancing its utility across complex real-world datasets. The third algorithm introduces an innovative analytical method for reconstructing mini-batches, addressing a critical gap in current research that predominantly focuses on reconstructing only a single training example. Unlike previous studies that focused mainly on the weight constraints of convolutional layers, our approach emphasizes the pivotal role of gradient constraints, revealing that successful attacks can be executed with fewer than 5% of the constraints previously deemed necessary in certain layers.

Problem

• Reconstructing private training data from gradients in federated learning systems
• Overcoming limitations of existing attacks on convolutional layers and high-dimensional inputs
• Developing analytical methods to recover multiple training examples from mini-batches

Innovation

• Analytical gradient inversion for convolutional layers
• High-dimensional input reconstruction from gradients
• Mini-batch analytical reconstruction method