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Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data.
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
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Svd approach to data unfolding
Hoecker, A. and Kartvelishvili, V · 1996
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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Chen, Y. and Wainwright, M. J · 2015
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Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (sgd)
Qian, Q., Jin, R., Yi, J., Zhang, L., and Zhu, S · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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The EU General Data Protection Regulation (GDPR): A Practical Guide
Bussche, A · 2017
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Badnets: Identifying vulnerabilities in the machine learning model supply chain
Gu, T., Dolan-Gavitt, B., and Garg, S · 2017
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Detecting cancer metastases on gigapixel pathology images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Comprehensive privacy analysis of deep learning
Nasr, M., Shokri, R., and Houmansadr, A · 2018
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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The california consumer privacy act: Towards a european-style privacy regime in the united states
Pardau, S. L · 2018
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Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y · 2019
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Exploiting unintended feature leakage in collaborative learning
Melis, L., Song, C., De Cristofaro, E., and Shmatikov, V · 2019
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The seven sins of { \{ Personal-Data } \} processing systems under { \{ GDPR } \}
Shastri, S., Wasserman, M., and Chidambaram, V · 2019
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A hybrid approach to privacy-preserving federated learning
Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
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Amnesiac machine learning
Graves, L., Nagisetty, V., and Ganesh, V · 2021
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Approximate data deletion from machine learning models
Izzo, Z., Smart, M. A., Chaudhuri, K., and Zou, J · 2021
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A survey on federated learning systems: vision, hype and reality for data privacy and protection
Li, Q., Wen, Z., Wu, Z., Hu, S., Wang, N., Li, Y., Liu, X., and He, B · 2021
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Federaser: Enabling efficient client-level data removal from federated learning models
Liu, G., Ma, X., Yang, Y., Wang, C., and Liu, J · 2021
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Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y · 2019
Cited alongside, same era.
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2020
Cited alongside, same era.
Orthogonal gradient descent for continual learning
Farajtabar, M., Azizan, N., Mott, A., and Li, A · 2020
Cited alongside, same era.
The limitations of federated learning in sybil settings
Fung, C., Yoon, C. J., and Beschastnikh, I · 2020
Cited alongside, same era.
Analyzing user-level privacy attack against federated learning
Song, M., Wang, Z., Zhang, Z., Song, Y., Wang, Q., Ren, J., and Qi, H · 2020
Cited alongside, same era.
Deltagrad: Rapid retraining of machine learning models
Wu, Y., Dobriban, E., and Davidson, S · 2020
Cited alongside, same era.
Dba: Distributed backdoor attacks against federated learning
Xie, C., Huang, K., Chen, P.-Y., and Li, B · 2020
Cited alongside, same era.
Saha, G., Garg, I., and Roy, K · 2021
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Remember what you want to forget: Algorithms for machine unlearning
Sekhari, A., Acharya, J., Kamath, G., and Suresh, A. T · 2021
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., and Zeppelzauer, M · 2022
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Federated unlearning: How to efficiently erase a client in fl?
Halimi, A., Kadhe, S., Rawat, A., and Baracaldo, N · 2022
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Subspace learning for effective meta-learning
Jiang, W., Kwok, J., and Zhang, Y · 2022
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Unrolling sgd: Understanding factors influencing machine unlearning
Thudi, A., Deza, G., Chandrasekaran, V., and Papernot, N · 2022
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Federated unlearning via class-discriminative pruning
Wang, J., Guo, S., Xie, X., and Qi, H · 2022
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Federated unlearning with knowledge distillation
Wu, C., Zhu, S., and Mitra, P · 2022
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