Medical Image Privacy in Federated Learning: Segmentation-Reorganization and Sparsified Gradient Matching Attacks.

In modern medicine, the widespread use of medical imaging has greatly improved diagnostic and treatment efficiency. However, these images contain sensitive personal information, and any leakage could seriously compromise patient privacy, leading to ethical and legal issues. Federated learning (FL), an emerging privacy-preserving technique, transmits gradients rather than raw data for model training. Yet, recent studies reveal that gradient inversion attacks can exploit this information to reconstruct private data, posing a significant threat to FL. Current attacks remain limited in image resolution, similarity, and batch processing, and thus do not yet pose a significant risk to FL. To address this, we propose a novel gradient inversion attack based on sparsified gradient matching and segmentation reorganization (SR) to reconstruct high-resolution, high-similarity medical images in batch mode. Specifically, an $L_{1}$ loss function optimises the gradient sparsification process, while the SR strategy enhances image resolution. An adaptive learning rate adjustment mechanism is also employed to improve optimisation stability and avoid local optima. Experimental results demonstrate that our method significantly outperforms state-of-the-art approaches in both visual quality and quantitative metrics, achieving up to a 146% improvement in similarity.