Abstract
Machine Unlearning (MU) is an emerging discipline studying methods to remove the effect of a data instance on the parameters of a trained model. Federated Unlearning (FU) extends MU to unlearn the contribution of a dataset provided by a client wishing to drop from a federated learning study. Due to the emerging nature of FU, a practical assessment of the effectiveness of the currently available approaches in complex medical imaging tasks has not been studied so far. In this work, we propose the first in-depth study of FU in medical imaging, with a focus on collaborative prostate segmentation from multi-centric MRI dataset. We first verify the unlearning capabilities of a panel of FU methods from the state-of-the-art, including approaches based on model adaptation, differential privacy, and adaptive retraining. For each method, we quantify their unlearning effectiveness and computational cost as compared to the baseline retraining of a model from scratch after client dropout. Our work highlights a new perspective for the practical implementation of data regulations in collaborative medical imaging applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Voigt, P., von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7
Harding, E.L., Vanto, J.J., Clark, R., Hannah Ji, L., Ainsworth, S.C.: Understanding the scope and impact of the California consumer privacy act of 2018. J. Data Protect. Priv. 2(3), 234–253 (2019)
Nguyen, T.T., et al.: A survey of machine unlearning. arXiv (2022)
Brisimi, T.S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I.C., Shi, W.: Federated learning of predictive models from federated electronic health records. Int. J. Med. Inf. 112, 59–67 (2018)
Silva, S., Gutman, B.A., Romero, E., Thompson, P.M., Altmann, A., Lorenzi, M.: Federated learning in distributed medical databases: meta-analysis of large-scale subcortical brain data. In: 2019 IEEE 16th International Symposium on Biomedical Imaging, IEEE (2019)
Liu, G., Ma, X., Yang, Y., Wang, C., Liu, J.: Federaser: enabling efficient client-level data removal from federated learning models. In: 2021 International Symposium on Quality of Service (2021)
Wu, C., Zhu, S., Mitra, P.: Federated unlearning with knowledge distillation. arXiv preprint arXiv:2201.09441 (2022)
Halimi, A., Kadhe, S., Rawat, A., Baracaldo, N.: Federated unlearning: how to efficiently erase a client in FL? arXiv (2022)
Fraboni, Y., Vidal, R., Kameni, L., Lorenzi, M.: Efficient and provable client unlearning in federated optimization, Sequential informed federated unlearning (2022)
LeCun, Y., Bottou, L., Bengio, Y., Ha, P.: LeNet. In: Proceedings of the IEEE (1998)
Krizhevsky, A.: Learning multiple layers of features from tiny images (2009)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of ICCV 2015 (2015)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: ICML 2017 (2017)
Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of FedAvg on Non-IID data. In: ICLR 2020 (2020)
Khaled, A., Mishchenko, K., Richtárik, P.: Tighter theory for local SGD on identical and heterogeneous data. In: AISTATS 2020 (2020)
Guo, C., Goldstein, T., Hannun, A., Van Der Maaten, L.: Certified data removal from machine learning models. In: ICML 2020 (2020)
Izzo, Z., Smart, M.A., Chaudhuri, K., Zou, J.: Approximate data deletion from machine learning models. In: AISTATS 2021 (2021)
Golatkar, A., Achille, A., Soatto, S.: Eternal sunshine of the spotless net: selective forgetting in deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
Golatkar, A., Achille, A., Soatto, S.: Scrubbing deep networks of information accessible from input-output observations, Forgetting outside the box (2020)
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., Soatto, S.: Mixed-privacy forgetting in deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Mahadevan, A., Mathioudakis, M.: Certifiable machine unlearning for linear models. CoRR, abs/2106.15093 (2021)
Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. In: NeurIPS 2019 (2019)
Neel, S., Roth, A., Sharifi-Malvajerdi, S.: Descent-to-delete: Gradient-based methods for machine unlearning. In: Proceedings of the 32nd International Conference on Algorithmic Learning Theory, PMLR (2021)
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., Waites, C.: Adaptive machine unlearning. In: Advances in Neural Information Processing Systems. Curran Associates Inc (2021)
Liu, Y., Xu, L., Yuan, X., Wang, C., Li, B.: The right to be forgotten in federated learning: an efficient realization with rapid retraining. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications (2022)
Wang, J., Guo, S., Xie, X., Qi, H.: Federated unlearning via class-discriminative pruning. In: Proceedings of the ACM Web Conference 2022 (2021)
Gao, X., et al.: Towards verifiable federated unlearning. arXiv, Verifi (2022)
Leijie, W., Guo, S., Wang, J., Hong, Z., Zhang, J., Ding, Y.: Federated unlearning: guarantee the right of clients to forget. IEEE Network 36(5), 129–135 (2022)
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)
Koshkin, V.S., et al.: Promise: a real-world clinical-genomic database to address knowledge gaps in prostate cancer. Prostate cancer and prostatic diseases (2022)
Armato, S.G., III., et al.: Prostatex challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J. Med. Imaging 5(4), 044501–044501 (2018)
Cuocolo, R., Stanzione, A., Castaldo, A., Lucia, D.R.D., Imbriaco, M.: Quality control and whole-gland, zonal and lesion annotations for the prostatex challenge public dataset. Eur. J. Radiol. 138, 109647 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
A Additional Experiments and Experimental Details
A Additional Experiments and Experimental Details
Prediction Mask on a slice of a sample MRI from center \(C_2\), where FU is applied to the data of \(C_2\).
Prediction Mask on a slice of a sample MRI from center \(C_3\), where FU is applied to the data of \(C_2\).
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Fraboni, Y. et al. (2023). Validation of Federated Unlearning on Collaborative Prostate Segmentation. In: Celebi, M.E., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops . MICCAI 2023. Lecture Notes in Computer Science, vol 14393. Springer, Cham. https://doi.org/10.1007/978-3-031-47401-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-47401-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47400-2
Online ISBN: 978-3-031-47401-9
eBook Packages: Computer ScienceComputer Science (R0)
