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2024

A Survey on Federated Unlearning: Challenges and Opportunities

Jeong, Hyejun, Ma, Shiqing, Houmansadr, Amir

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Federated learning (FL), introduced in 2017, facilitates collaborative learning between non-trusting parties with no need for the parties to explicitly share their data among themselves.

This allows training models on user data while respecting privacy regulations such as GDPR and CPRA.
However, emerging privacy requirements may mandate model owners to be able to \emph{forget} some learned data, e.g., when requested by data owners or law enforcement.
This has given birth to an active field of research called \emph{machine unlearning}.

A Survey on Federated Unlearning: Challenges and Opportunities · Around

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Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in 2015 IEEE symposium on security and privacy . IEEE, 2015, pp. 463–480
2015
Earlier work this paper cites.
B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics . PMLR, 2017, pp. 1273–1282
2017
Earlier work this paper cites.
P. Blanchard, E. M. El Mhamdi, R. Guerraoui, and J. Stainer, “Machine learning with adversaries: Byzantine tolerant gradient descent,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, and V. Chandra, “Federated learning with non-iid data,” arXiv preprint arXiv:1806.00582 , 2018
Original
2018
Earlier work this paper cites.
D. Yin, Y. Chen, R. Kannan, and P. Bartlett, “Byzantine-robust distributed learning: Towards optimal statistical rates,” in International Conference on Machine Learning . PMLR, 2018, pp. 5650–5659
2018
Earlier work this paper cites.
S. Schelter, “amnesia–towards machine learning models that can forget user data very fast,” in 1st International Workshop on Applied AI for Database Systems and Applications (AIDB19) , 2019
2019
Earlier work this paper cites.
S. Misra and Y. Wu, “Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking,” Machine learning for subsurface characterization , vol. 289, no. 3, p. 4, 2019
2019
Earlier work this paper cites.
Y. Liu, Z. Ma, X. Liu, J. Liu, Z. Jiang, J. Ma, P. Yu, and K. Ren, “Learn to forget: Machine unlearning via neuron masking,” arXiv preprint arXiv:2003.10933 , 2020
Original
2020
Earlier work this paper cites.
M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to { \{ Byzantine-Robust } \} federated learning,” in 29th USENIX security symposium (USENIX Security 20) , 2020, pp. 1605–1622
2020
Earlier work this paper cites.
E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” in International conference on artificial intelligence and statistics . PMLR, 2020, pp. 2938–2948
2020
Earlier work this paper cites.
G. Liu, X. Ma, Y. Yang, C. Wang, and J. Liu, “Federaser: Enabling efficient client-level data removal from federated learning models,” in 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) . IEEE, 2021, pp. 1–10
2021
Earlier work this paper cites.
L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine unlearning,” in 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 2021, pp. 141–159
2021
Earlier work this paper cites.
L. Graves, V. Nagisetty, and V. Ganesh, “Amnesiac machine learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 13, 2021, pp. 11 516–11 524
2021
Earlier work this paper cites.
H. Huang, X. Ma, S. M. Erfani, J. Bailey, and Y. Wang, “Unlearnable examples: Making personal data unexploitable,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
A. Peste, D. Alistarh, and C. H. Lampert, “SSSE: Efficiently erasing samples from trained machine learning models,” in NeurIPS 2021 Workshop Privacy in Machine Learning , 2021
2021
Earlier work this paper cites.
Y. Liu, Z. Ma, Y. Yang, X. Liu, J. Ma, and K. Ren, “Revfrf: Enabling cross-domain random forest training with revocable federated learning,” IEEE Transactions on Dependable and Secure Computing , vol. 19, no. 6, pp. 3671–3685, 2021
2021
Earlier work this paper cites.
P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al. , “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning , vol. 14, no. 1–2, pp. 1–210, 2021
2021
Earlier work this paper cites.
T. T. Nguyen, T. T. Huynh, P. L. Nguyen, A. W.-C. Liew, H. Yin, and Q. V. H. Nguyen, “A survey of machine unlearning,” arXiv preprint arXiv:2209.02299 , 2022
Original
2022
Earlier work this paper cites.
J. Nguyen, K. Malik, H. Zhan, A. Yousefpour, M. Rabbat, M. Malek, and D. Huba, “Federated learning with buffered asynchronous aggregation,” in International conference on artificial intelligence and statistics . PMLR, 2022, pp. 3581–3607
2022
Earlier work this paper cites.
L. Wu, S. Guo, J. Wang, Z. Hong, J. Zhang, and Y. Ding, “Federated unlearning: Guarantee the right of clients to forget,” IEEE Network , vol. 36, no. 5, pp. 129–135, 2022
2022
Earlier work this paper cites.
J. Gong, O. Simeone, and J. Kang, “Compressed particle-based federated bayesian learning and unlearning,” IEEE Communications Letters , vol. 27, no. 2, pp. 556–560, 2022
2022
Earlier work this paper cites.
Y. Liu, L. Xu, X. Yuan, C. Wang, and B. Li, “The right to be forgotten in federated learning: An efficient realization with rapid retraining,” in IEEE INFOCOM 2022-IEEE Conference on Computer Communications . IEEE, 2022, pp. 1749–1758
2022
Earlier work this paper cites.
C. Pan, J. Sima, S. Prakash, V. Rana, and O. Milenkovic, “Machine unlearning of federated clusters,” arXiv preprint arXiv:2210.16424 , 2022
Original
2022
Earlier work this paper cites.
J. Gong, J. Kang, O. Simeone, and R. Kassab, “Forget-svgd: Particle-based bayesian federated unlearning,” in 2022 IEEE Data Science and Learning Workshop (DSLW) . IEEE, 2022, pp. 1–6
2022
Earlier work this paper cites.
N. K. Dinsdale, M. Jenkinson, and A. I. Namburete, “Fedharmony: unlearning scanner bias with distributed data,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 695–704
2022
Earlier work this paper cites.
A. Halimi, S. Kadhe, A. Rawat, and N. Baracaldo, “Federated unlearning: How to efficiently erase a client in fl?” arXiv preprint arXiv:2207.05521 , 2022
Original
2022
Earlier work this paper cites.
J. Wang, S. Guo, X. Xie, and H. Qi, “Federated unlearning via class-discriminative pruning,” in Proceedings of the ACM Web Conference , 2022, pp. 622–632
2022
Earlier work this paper cites.
R. Mehta, S. Pal, V. Singh, and S. N. Ravi, “Deep unlearning via randomized conditionally independent hessians,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 422–10 431
2022
Earlier work this paper cites.
Z. Zhang, A. Panda, L. Song, Y. Yang, M. Mahoney, P. Mittal, R. Kannan, and J. Gonzalez, “Neurotoxin: Durable backdoors in federated learning,” in International Conference on Machine Learning . PMLR, 2022, pp. 26 429–26 446
2022
Earlier work this paper cites.
Q. Li, Y. Diao, Q. Chen, and B. He, “Federated learning on non-iid data silos: An experimental study,” in 2022 IEEE 38th International Conference on Data Engineering (ICDE) . IEEE, 2022, pp. 965–978
Original
2022
Earlier work this paper cites.
X. Cao, J. Jia, Z. Zhang, and N. Z. Gong, “Fedrecover: Recovering from poisoning attacks in federated learning using historical information,” in 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 2023, pp. 1366–1383
2023
Earlier work this paper cites.
H. Xu, T. Zhu, L. Zhang, W. Zhou, and P. S. Yu, “Machine unlearning: A survey,” ACM Computing Surveys , vol. 56, no. 1, pp. 1–36, 2023
2023
Earlier work this paper cites.
L. Wu, S. Guo, J. Wang, Z. Hong, J. Zhang, and J. Zhou, “On knowledge editing in federated learning: Perspectives, challenges, and future directions,” arXiv preprint arXiv:2306.01431 , 2023
Original
2023
Earlier work this paper cites.
F. Wang, B. Li, and B. Li, “Federated unlearning and its privacy threats,” IEEE Network , 2023
2023
Earlier work this paper cites.
J. Yang and Y. Zhao, “A survey of federated unlearning: A taxonomy, challenges and future directions,” arXiv preprint arXiv:2310.19218 , 2023
Original
2023
Earlier work this paper cites.
Z. Liu, Y. Jiang, J. Shen, M. Peng, K.-Y. Lam, X. Yuan, and X. Liu, “A survey on federated unlearning: Challenges, methods, and future directions,” ACM Computing Surveys , 2023
2023
Earlier work this paper cites.
L. Zhang, T. Zhu, H. Zhang, P. Xiong, and W. Zhou, “Fedrecovery: Differentially private machine unlearning for federated learning frameworks,” IEEE Transactions on Information Forensics and Security , 2023
2023
Earlier work this paper cites.
N. Su and B. Li, “Asynchronous federated unlearning,” in IEEE INFOCOM 2023-IEEE Conference on Computer Communications . IEEE, 2023, pp. 1–10
2023
Earlier work this paper cites.
R.-Z. Xu, S.-Y. Hong, P.-W. Chi, and M.-H. Wang, “A revocation key-based approach towards efficient federated unlearning,” in 2023 18th Asia Joint Conference on Information Security (AsiaJCIS) . IEEE, 2023, pp. 17–24
2023
Earlier work this paper cites.
W. Yuan, H. Yin, F. Wu, S. Zhang, T. He, and H. Wang, “Federated unlearning for on-device recommendation,” in Proceedings of the 16th ACM International Conference on Web Search and Data Mining , 2023, pp. 393–401
2023
Earlier work this paper cites.
R. Jin, M. Chen, Q. Zhang, and X. Li, “Forgettable federated linear learning with certified data removal,” arXiv preprint arXiv:2306.02216 , 2023
Original
2023
Earlier work this paper cites.
Y. Li, C. Chen, X. Zheng, and J. Zhang, “Federated unlearning via active forgetting,” arXiv preprint arXiv:2307.03363 , 2023
Original
2023
Earlier work this paper cites.

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