Merit, S., Xiong, C., Bradbury, J., and Socher, R. (2016). Pointer sentinel mixture models. arXiv preprint arXiv:1609.07843.
Pan, X., Zhang, M., Yan, Y., Zhu, J., and Yang, Z. (2022). Exploring the security boundary of data reconstruction via neuron exclusivity analysis. In 31st USENIX Security Symposium (USENIX Security 22), pages 3989-4006.
Pruthi, G., Liu, F., Kale, S., and Sundararajan, M. (2020). Estimating training data influence by tracing gradient descent. Advances in Neural Information Processing Systems, 33:19920-19930.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I. (2019). Language models are unsupervised multitask learners.
Roth, H. R., Chang, K., Singh, P., Neumark, N., Li, W., Gupta, V., Gupta, S., Qu, L., Ihsani, A., Bizzo, B. C., et al. (2020). Federated learning for breast density classification: A real-world implementation. In *Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop*, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 2, pages 181–191. Springer.
Scheliga, D., Mäder, P., and Seeland, M. (2022). Precode-a generic model extension to prevent deep gradient leakage. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1849-1858.
Sun, J., Li, A., Wang, B., Yang, H., Li, H., and Chen, Y. (2020). Provable defense against privacy leakage in federated learning from representation perspective. arXiv preprint arXiv:2012.06043.
Wang, F., Hugh, E., and Li, B. (2023a). More than enough is too much: Adaptive defenses against gradient leakage in production federated learning. In INFOCOM.
Wang, Z., Lee, J., and Lei, Q. (2023b). Reconstructing training data from model gradient, provably. In International Conference on Artificial Intelligence and Statistics, pages 6595-6612. PMLR.
Wei, W., Liu, L., Wut, Y., Su, G., and Iyengar, A. (2021). Gradient-leakage resilient federated learning. In 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), pages 797-807. IEEE.
Wen, Y., Geiping, J., Fowl, L., Goldblum, M., and Goldstein, T. (2022). Fishing for user data in large-batch federated learning via gradient magnification. arXiv preprint arXiv:2202.00580.
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Lukasz Kaiser, Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., and Dean, J. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation.
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., and Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5:1-19.
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2017). mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412.
Zhang, H., Hong, J., Dong, F., Drew, S., Xue, L., and Zhou, J. (2023). A privacy-preserving hybrid federated learning framework for financial crime detection. arXiv preprint arXiv:2302.03654.
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595.
Zhao, B., Mopuri, K. R., and Bilen, H. (2020). idlg: Improved deep leakage from gradients. arXiv preprint arXiv:2001.02610.
Zhu, J. and Blaschko, M. B. (2021). R-gap: Recursive gradient attack on privacy. In International Conference on Learning Representations.
Zhu, L., Liu, Z., and Han, S. (2019). Deep leakage from gradients. Advances in neural information processing systems, 32.