Unifying Gradient Leakage Attacks Against Privacy-Protected Federated Learning in IoT Networks

Federated learning (FL) is a transformative paradigm for the Internet of Things (IoT), enabling decentralized model training across distributed IoT devices while reducing reliance on centralized data collection. Crucially, FL cuts communication overhead, an essential benefit in bandwidth-limited IoT environments. However, repeated gradient exchanges between edge clients (e.g., sensors, mobile devices) and the central server expose vulnerabilities to gradient leakage attacks (GLAs), allowing adversaries to reconstruct private training data from shared gradients. While various gradient protection strategies, such as differential privacy, sparsification, and clipping, have been introduced to mitigate this risk, most existing GLAs are designed for specific protection schemes and fail under heterogeneous deployments. In this work, we propose a unified GLA framework that targets diverse gradient protection techniques in FL systems. Our approach tackles two core challenges: (i) aligning protected gradients with their raw counterparts to enable robust feature extraction, and (ii) identifying critical features for efficient and accurate data reconstruction.We introduce a Taylor-based gradient approximation method for alignment and design a feature reconstructor that enhances both performance and computational efficiency. Extensive experiments across various FL scenarios demonstrate the framework’s superior reconstruction capability under different protection schemes, emphasizing the need for robust privacy-preserving mechanisms in IoT networks.