AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning
<|reference_start|>AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in Federated Learning: Federated learning (FL) empowers privacypreservation in model training by only exposing users' model gradients. Yet, FL users are susceptible to gradient inversion attacks (GIAs) which can reconstruct ground-truth training data such as images based on model gradients. However, reconstructing high-resolution images by existing GIAs faces two challenges: inferior accuracy and slow-convergence, especially when duplicating labels exist in the training batch. To address these challenges, we present an Accurate and Fast-convergent Gradient Inversion attack algorithm, called AFGI, with two components: Label Recovery Block (LRB) which can accurately restore duplicating labels of private images based on exposed gradients; VME Regularization Term, which includes the total variance of reconstructed images, the discrepancy between three-channel means and edges, between values from exposed gradients and reconstructed images, respectively. The AFGI can be regarded as a white-box attack strategy to reconstruct images by leveraging labels recovered by LRB. In particular, AFGI is efficient that accurately reconstruct ground-truth images when users' training batch size is up to 48. Our experimental results manifest that AFGI can diminish 85% time costs while achieving superb inversion quality in the ImageNet dataset. At last, our study unveils the shortcomings of FL in privacy-preservation, prompting the development of more advanced countermeasure strategies.<|reference_end|>
@article{liu2024afgi:,
    title={AFGI: Towards Accurate and Fast-convergent Gradient Inversion Attack in
  Federated Learning},
    author={Can Liu and Jin Wang and and Yipeng Zhou and Yachao Yuan and Quanzheng
  Sheng and Kejie Lu},
    journal={arXiv preprint arXiv:2403.08383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.08383},
    primaryClass={cs.CV}
}