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Federated Learning (FL) has received much attention in recent years.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
Steepest descent methods for multicriteria optimization
Fliege, J.; and Svaiter, B. F. 2000 · 2000
Earlier work this paper cites.
Steepest descent methods for multicriteria optimization
Fliege, J.; and Svaiter, B. F. 2000 · 2000
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
Earlier work this paper cites.
Multilayer perceptron and neural networks
Popescu, M.-C.; Balas, V. E.; Perescu-Popescu, L.; and Mastorakis, N. 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A.; and Hinton, G. 2009 · 2009
Earlier work this paper cites.
Multilayer perceptron and neural networks
Popescu, M.-C.; Balas, V. E.; Perescu-Popescu, L.; and Mastorakis, N. 2009 · 2009
Earlier work this paper cites.
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 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.
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 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.
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
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
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Li, T.; Sahu, A. K.; Talwalkar, A.; and Smith, V. 2020 · 2020
Earlier work this paper cites.
Federated learning: Challenges, methods, and future directions
Li, T.; Sahu, A. K.; Talwalkar, A.; and Smith, V. 2020 · 2020
Earlier work this paper cites.
Machine unlearning
Bourtoule, L.; Chandrasekaran, V.; Choquette-Choo, C. A.; Jia, H.; Travers, A.; Zhang, B.; Lie, D.; and Papernot, N. 2021 · 2021
Earlier work this paper cites.
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Brock, A.; De, S.; and Smith, S. L. 2021 · 2021
Cited alongside, same era.
Impact of colour on robustness of deep neural networks
De, K.; and Pedersen, M. 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.
Gradient projection memory for continual learning
Saha, G.; Garg, I.; and Roy, K. 2021 · 2021
Cited alongside, same era.
Federated Learning with Fair Averaging
Wang, Z.; Fan, X.; Qi, J.; Wen, C.; Wang, C.; and Yu, R. 2021 · 2021
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
Subspace based federated unlearning
Li, G.; Shen, L.; Sun, Y.; Hu, Y.; Hu, H.; and Tao, D. 2023 · 2023
Later among the works it cites.
A survey on federated unlearning: Challenges, methods, and future directions
Liu, Z.; Jiang, Y.; Shen, J.; Peng, M.; Lam, K.-Y.; and Yuan, X. 2023 · 2023
Later among the works it cites.
Fedmdfg: Federated learning with multi-gradient descent and fair guidance
Pan, Z.; Wang, S.; Li, C.; Wang, H.; Tang, X.; and Zhao, J. 2023 · 2023
Later among the works it cites.
Asynchronous federated unlearning
Su, N.; and Li, B. 2023 · 2023
Later among the works it cites.
A survey of federated unlearning: A taxonomy, challenges and future directions
Yang, J.; and Zhao, Y. 2023 · 2023
Later among the works it cites.
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Cited alongside, same era.
Characterizing signal propagation to close the performance gap in unnormalized ResNets
Brock, A.; De, S.; and Smith, S. L. 2021 · 2021
Cited alongside, same era.
Impact of colour on robustness of deep neural networks
De, K.; and Pedersen, M. 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.
Gradient projection memory for continual learning
Saha, G.; Garg, I.; and Roy, K. 2021 · 2021
Cited alongside, same era.
Federated Learning with Fair Averaging
Wang, Z.; Fan, X.; Qi, J.; Wen, C.; Wang, C.; and Yu, R. 2021 · 2021
Cited alongside, same era.
Federated unlearning: How to efficiently erase a client in fl?
Halimi, A.; Kadhe, S.; Rawat, A.; and Baracaldo, N. 2022 · 2022
Cited alongside, same era.
Fedrecovery: Differentially private machine unlearning for federated learning frameworks
Zhang, L.; Zhu, T.; Zhang, H.; Xiong, P.; and Zhou, W. 2023 · 2023
Later among the works it cites.
Federated unlearning with momentum degradation
Zhao, Y.; Wang, P.; Qi, H.; Huang, J.; Wei, Z.; and Zhang, Q. 2023 · 2023
Later among the works it cites.
Subspace based federated unlearning
Li, G.; Shen, L.; Sun, Y.; Hu, Y.; Hu, H.; and Tao, D. 2023 · 2023
Later among the works it cites.
A survey on federated unlearning: Challenges, methods, and future directions
Liu, Z.; Jiang, Y.; Shen, J.; Peng, M.; Lam, K.-Y.; and Yuan, X. 2023 · 2023
Later among the works it cites.
Fedmdfg: Federated learning with multi-gradient descent and fair guidance
Pan, Z.; Wang, S.; Li, C.; Wang, H.; Tang, X.; and Zhao, J. 2023 · 2023
Later among the works it cites.
Asynchronous federated unlearning
Su, N.; and Li, B. 2023 · 2023
Later among the works it cites.
A survey of federated unlearning: A taxonomy, challenges and future directions
Yang, J.; and Zhao, Y. 2023 · 2023
Later among the works it cites.
Fedrecovery: Differentially private machine unlearning for federated learning frameworks
Zhang, L.; Zhu, T.; Zhang, H.; Xiong, P.; and Zhou, W. 2023 · 2023
Later among the works it cites.
Federated unlearning with momentum degradation
Zhao, Y.; Wang, P.; Qi, H.; Huang, J.; Wei, Z.; and Zhang, Q. 2023 · 2023
Later among the works it cites.
FedLF: Layer-Wise Fair Federated Learning
Pan, Z.; Li, C.; Yu, F.; Wang, S.; Wang, H.; Tang, X.; and Zhao, J. 2024 · 2024
Closest in time.
Heterogeneous decentralised machine unlearning with seed model distillation
Ye, G.; Chen, T.; Hung Nguyen, Q. V.; and Yin, H. 2024 · 2024
Closest in time.
FedLF: Layer-Wise Fair Federated Learning
Pan, Z.; Li, C.; Yu, F.; Wang, S.; Wang, H.; Tang, X.; and Zhao, J. 2024 · 2024
Closest in time.
Heterogeneous decentralised machine unlearning with seed model distillation
Ye, G.; Chen, T.; Hung Nguyen, Q. V.; and Yin, H. 2024 · 2024
Closest in time.