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Federated Learning (FL) enables collaborative deep learning training across multiple participants without exposing sensitive personal data.
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C. Fung, C. J. M. Yoon, and I. Beschastnikh, “The Limitations of Federated Learning in Sybil Settings,” in 23rd International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2020, San Sebastian, Spain, October 14-15, 2020
2020
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N. Rieke, J. Hancox, W. Li, F. Milletarì, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. H. Maier-Hein, S. Ourselin, M. J. Sheller, R. M. Summers, A. Trask, D. Xu, M. Baust, and M. J. Cardoso, “The future of digital health with federated learning,” npj Digit. Medicine
2020
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C. Yang, Q. Wang, M. Xu, Z. Chen, K. Bian, Y. Liu, and X. Liu, “Characterizing Impacts of Heterogeneity in Federated Learning upon Large-Scale Smartphone Data,” in WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021
2021
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M. S. Özdayi, M. Kantarcioglu, and Y. R. Gel, “Defending against Backdoors in Federated Learning with Robust Learning Rate,” in Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021
2021
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2021
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S. Andreina, G. A. Marson, H. Möllering, and G. Karame, “BaFFLe: Backdoor Detection via Feedback-based Federated Learning,” in 41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021, Washington DC, USA, July 7-10, 2021
2021
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C. Zhao, Y. Wen, S. Li, F. Liu, and D. Meng, “FederatedReverse: A Detection and Defense Method Against Backdoor Attacks in Federated Learning,” in IH&MMSec ’21: ACM Workshop on Information Hiding and Multimedia Security, Virtual Event, Belgium, June, 22-25, 2021
2021
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L. Bourtoule, V. Chandrasekaran, C. A. Choquette-Choo, H. Jia, A. Travers, B. Zhang, D. Lie, and N. Papernot, “Machine Unlearning,” in 42nd IEEE Symposium on Security and Privacy, SP 2021, San Francisco, CA, USA, 24-27 May 2021
2021
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2022
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2022
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2022
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L. Miao, W. Yang, R. Hu, L. Li, and L. Huang, “Against Backdoor Attacks In Federated Learning With Differential Privacy,” in IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022
2022
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A. Panda, S. Mahloujifar, A. N. Bhagoji, S. Chakraborty, and P. Mittal, “SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification,” in International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event
2022
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2022
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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, London, United Kingdom, May 2-5, 2022
2022
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L. Wu, S. Guo, J. Wang, Z. Hong, J. Zhang, and Y. Ding, “Federated Unlearning: Guarantee the Right of Clients to Forget,” IEEE Netw
2022
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2022
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Y. Liu, M. Fan, C. Chen, X. Liu, Z. Ma, L. Wang, and J. Ma, “Backdoor Defense with Machine Unlearning,” in IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, London, United Kingdom, May 2-5, 2022
2022
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2022
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N. G. Marchant, B. I. P. Rubinstein, and S. Alfeld, “Hard to Forget: Poisoning Attacks on Certified Machine Unlearning,” in Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022
2022
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V. Shejwalkar, A. Houmansadr, P. Kairouz, and D. Ramage, “Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning,” in 43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022
2022
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J. Wang, S. Guo, X. Xie, and H. Qi, “Federated Unlearning via Class-Discriminative Pruning,” in WWW ’22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022
2022
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2022
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C. Wu, S. Zhu, and P. Mitra, “Federated Unlearning with Knowledge Distillation,” CoRR
2022
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2023
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P. Fang and J. Chen, “On the Vulnerability of Backdoor Defenses for Federated Learning,” CoRR
2023
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A. Warnecke, L. Pirch, C. Wressnegger, and K. Rieck, “Machine Unlearning of Features and Labels,” in 30th Annual Network and Distributed System Security Symposium, NDSS 2023, San Diego, California, USA, February 27 - March 3, 2023
2023
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W. Yuan, H. Yin, F. Wu, S. Zhang, T. He, and H. Wang, “Federated Unlearning for On-Device Recommendation,” in Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining, WSDM 2023, Singapore, 27 February 2023 - 3 March 2023
2023
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