Fetching the paper…
Reading the bibliography…
Federated Unlearning (FU) enables clients to selectively remove the influence of specific data from a trained federated learning model, addressing privacy concerns and regulatory requirements.
Backpropagation and stochastic gradient descent method
Shun-ichi Amari · 1993
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
Seok-Ju Hahn and Junghye Lee · 2019
Earlier work this paper cites.
Understanding the scope and impact of the california consumer privacy act of 2018
Elizabeth Liz Harding, Jarno J Vanto, Reece Clark, L Hannah Ji, and Sara C Ainsworth · 2019
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Earlier work this paper cites.
Stochastic channel-based federated learning for medical data privacy preserving
Rulin Shao, Hongyu He, Hui Liu, and Dianbo Liu · 2019
Earlier work this paper cites.
Differentially private secure multi-party computation for federated learning in financial applications
David Byrd and Antigoni Polychroniadou · 2020
Earlier work this paper cites.
Federated optimization in heterogeneous networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith · 2020
Earlier work this paper cites.
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2020
Earlier work this paper cites.
Deltagrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
Earlier work this paper cites.
Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
Cited alongside, same era.
Machine unlearning for random forests
Jonathan Brophy and Daniel Lowd · 2021
Cited alongside, same era.
Kongyang Chen, Yao Huang, and Yiwen Wang · 2021
Cited alongside, same era.
Amnesiac machine learning
Laura Graves, Vineel Nagisetty, and Vijay Ganesh · 2021
Cited alongside, same era.
Adaptive machine unlearning
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites · 2021
Cited alongside, same era.
Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
Federated and privacy-preserving learning of accounting data in financial statement audits
Marco Schreyer, Timur Sattarov, and Damian Borth · 2022
Later among the works it cites.
On the necessity of auditable algorithmic definitions for machine unlearning
Anvith Thudi, Hengrui Jia, Ilia Shumailov, and Nicolas Papernot · 2022
Later among the works it cites.
Federated unlearning via class-discriminative pruning
Junxiao Wang, Song Guo, Xin Xie, and Heng Qi · 2022
Later among the works it cites.
Federated unlearning: Guarantee the right of clients to forget
Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding · 2022
Later among the works it cites.
Fast federated machine unlearning with nonlinear functional theory
Tianshi Che, Yang Zhou, Zijie Zhang, Lingjuan Lyu, Ji Liu, Da Yan, Dejing Dou, and Jun Huan · 2023
Later among the works it cites.
Fedmcsa: Personalized federated learning via model components self-attention
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Federaser: Enabling efficient client-level data removal from federated learning models
Gaoyang Liu, Xiaoqiang Ma, Yang Yang, Chen Wang, and Jiangchuan Liu · 2021
Cited alongside, same era.
No fear of heterogeneity: Classifier calibration for federated learning with non-iid data
Mi Luo, Fei Chen, Dapeng Hu, Yifan Zhang, Jian Liang, and Jiashi Feng · 2021
Cited alongside, same era.
Hedgecut: Maintaining randomised trees for low-latency machine unlearning
Sebastian Schelter, Stefan Grafberger, and Ted Dunning · 2021
Cited alongside, same era.
Machine unlearning: Linear filtration for logit-based classifiers
Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer · 2022
Cited alongside, same era.
Dual class-aware contrastive federated semi-supervised learning
Qi Guo, Yong Qi, Saiyu Qi, and Di Wu · 2022
Cited alongside, same era.
Flmjr: Improving robustness of federated learning via model stability
Qi Guo, Di Wu, Yong Qi, Saiyu Qi, and Qian Li · 2022
Cited alongside, same era.
Qi Guo, Yong Qi, Saiyu Qi, Di Wu, and Qian Li · 2023
Later among the works it cites.
A survey on federated unlearning: Challenges, methods, and future directions
Ziyao Liu, Yu Jiang, Jiyuan Shen, Minyi Peng, Kwok-Yan Lam, Xingliang Yuan, and Xiaoning Liu · 2023
Later among the works it cites.
Asynchronous federated unlearning
Ningxin Su and Baochun Li · 2023
Later among the works it cites.
Fast yet effective machine unlearning
Ayush K Tarun, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli · 2023
Later among the works it cites.
A survey of federated unlearning: A taxonomy, challenges and future directions
Jiaxi Yang and Yang Zhao · 2023
Later among the works it cites.
Federated unlearning for on-device recommendation
Wei Yuan, Hongzhi Yin, Fangzhao Wu, Shijie Zhang, Tieke He, and Hao Wang · 2023
Later among the works it cites.
Federated unlearning with momentum degradation
Yian Zhao, Pengfei Wang, Heng Qi, Jianguo Huang, Zongzheng Wei, and Qiang Zhang · 2023
Later among the works it cites.
Qi Guo, Minghao Yao, Zhen Tian, Saiyu Qi, Yong Qi, Yun Lin, and Jin Song Dong · 2024
Closest in time.
Towards unbounded machine unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes, and Eleni Triantafillou · 2024
Closest in time.