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Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure.
Measuring the effects of non-identical data distribution for federated visual classification
Harry Hsu, T. M., Qi, H., and Brown, M. (2019) · 1909
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Towards probabilistic verification of machine unlearning
Sommer, D. M., Song, L., Wagh, S., and Mittal, P. (2020) · 2003
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Learning multiple layers of features from tiny images
Krizhevsky, A. (2009) · 2009
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A. (2014) · 2014
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J. (2015) · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X. (2015) · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Hardt, M., Recht, B., and Singer, Y. (2016) · 2016
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Approximate data deletion from machine learning models
Izzo, Z., Anne Smart, M., Chaudhuri, K., and Zou, J. (2021) · 2016
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Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A. (2017) · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Voigt, P. and Von dem Bussche, A. (2017) · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R. (2017) · 2017
Earlier work this paper cites.
LEAF: A Benchmark for Federated Settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečný, J., McMahan, H. B., Smith, V., and Talwalkar, A. (2018) · 2018
Earlier work this paper cites.
Understanding the scope and impact of the california consumer privacy act of 2018
Harding, E. L., Vanto, J. J., Clark, R., Hannah Ji, L., and Ainsworth, S. C. (2019) · 2018
Cited alongside, same era.
Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y. (2019) · 2019
Cited alongside, same era.
Understanding gradient clipping in private sgd: A geometric perspective
Chen, X., Wu, S. Z., and Hong, M. (2020) · 2020
Cited alongside, same era.
Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L. (2020) · 2020
Cited alongside, same era.
On the convergence of fedavg on non-iid data
Li, X., Huang, K., Yang, W., Wang, S., and Zhang, Z. (2020) · 2020
Cited alongside, same era.
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V. (2020) · 2020
Certifiable machine unlearning for linear models
Mahadevan, A. and Mathioudakis, M. (2021) · 2021
Later among the works it cites.
Descent-to-delete: Gradient-based methods for machine unlearning
Neel, S., Roth, A., and Sharifi-Malvajerdi, S. (2021) · 2021
Later among the works it cites.
Forget-svgd: Particle-based bayesian federated unlearning
Gong, J., Kang, J., Simeone, O., and Kassab, R. (2022) · 2022
Closest in time.
Federated unlearning: How to efficiently erase a client in fl?
Halimi, A., Kadhe, S., Rawat, A., and Baracaldo, N. (2022) · 2022
Closest in time.
The right to be forgotten in federated learning: An efficient realization with rapid retraining
Liu, Y., Xu, L., Yuan, X., Wang, C., and Li, B. (2022) · 2022
Closest in time.
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Cited alongside, same era.
Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N. (2021) · 2021
Cited alongside, same era.
Mixed-privacy forgetting in deep networks
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., and Soatto, S. (2021) · 2021
Cited alongside, same era.
Adaptive machine unlearning
Gupta, V., Jung, C., Neel, S., Roth, A., Sharifi-Malvajerdi, S., and Waites, C. (2021) · 2021
Cited alongside, same era.
Online forgetting process for linear regression models
Li, Y., Wang, C., and Cheng, G. (2021) · 2021
Cited alongside, same era.
Federaser: Enabling efficient client-level data removal from federated learning models
Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J. (2021) · 2021
Cited alongside, same era.
Eternal sunshine of the spotless net: Selective forgetting in deep networks
Golatkar, A., Achille, A., and Soatto, S. (2020a)
Cited in the paper.
Federated unlearning via class-discriminative pruning
Wang, J., Guo, S., Xie, X., and Qi, H. (2022) · 2022
Closest in time.
Federated unlearning with knowledge distillation
Wu, C., Zhu, S., and Mitra, P. (2022) · 2022
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Improved gradient inversion attacks and defenses in federated learning
Geng, J., Mou, Y., Li, Q., Li, F., Beyan, O., Decker, S., and Rong, C. (2023) · 2023
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Forgettable federated linear learning with certified data removal
Jin, R., Chen, M., Zhang, Q., and Li, X. (2023) · 2023
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Machine unlearning of federated clusters
Pan, C., Sima, J., Prakash, S., Rana, V., and Milenkovic, O. (2023) · 2023
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Bfu: Bayesian federated unlearning with parameter self-sharing
Wang, W., Tian, Z., Zhang, C., Liu, A., and Yu, S. (2023) · 2023
Closest in time.