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In recent years, the notion of ``the right to be forgotten" (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their requests.
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Federated Unlearning: How to Efficiently Erase a Client in FL?. In International Conference on Machine Learning
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Learn to forget: Machine unlearning via neuron masking
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A survey of machine unlearning
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Machine Unlearning of Federated Clusters. In The Eleventh International Conference on Learning Representations
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Virat Shejwalkar, Amir Houmansadr, Peter Kairouz, and Daniel Ramage. 2022 · 2022
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Federated unlearning via class-discriminative pruning. In Proceedings of the ACM Web Conference 2022 . 622–632
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Model extraction attacks on graph neural networks: Taxonomy and realisation. In Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security . 337–350
Bang Wu, Xiangwen Yang, Shirui Pan, and Xingliang Yuan. 2022a · 2022
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Prompt certified machine unlearning with randomized gradient smoothing and quantization
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Aggregation service for federated learning: An efficient, secure, and more resilient realization
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Get Rid Of Your Trail: Remotely Erasing Backdoors in Federated Learning
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Federated Unlearning and Server Right to Forget: Handling Unreliable Client Contributions. In International Conference on Recent Trends in Image Processing and Pattern Recognition . Springer, 393–410
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