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Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants.
Local Differential Privacy: a tutorial
Bebensee, B. 2019 · 1907
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Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. 1965 · 1965
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iDLG: Improved Deep Leakage from Gradients
Zhao, B.; Mopuri, K. R.; and Bilen, H. 2020 · 2001
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Calibrating noise to sensitivity in private data analysis
Dwork, C.; McSherry, F.; Nissim, K.; and Smith, A. 2006 · 2006
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Mechanism Design via Differential Privacy
McSherry, F.; and Talwar, K. 2007 · 2007
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SplitNN-driven Vertical Partitioning
Ceballos, I.; Sharma, V.; Mugica, E.; Singh, A.; Roman, A.; Vepakomma, P.; and Raskar, R. 2020 · 2008
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Kasiviswanathan, S. P.; Lee, H. K.; Nissim, K.; Raskhodnikova, S.; and Smith, A. D. 2008 · 2008
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Boosting and Differential Privacy
Dwork, C.; Rothblum, G. N.; and Vadhan, S. 2010 · 2010
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Local Privacy and Statistical Minimax Rates
Duchi, J. C.; Jordan, M. I.; and Wainwright, M. J. 2013 · 2013
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RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
Erlingsson, Ú.; Pihur, V.; and Korolova, A. 2014 · 2014
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Differentially Private Algorithms for Empirical Machine Learning
Stoddard, B.; Chen, Y.; and Machanavajjhala, A. 2014 · 2014
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Learning from Corrupted Binary Labels via Class-Probability Estimation
Menon, A.; Rooyen, B. V.; Ong, C. S.; and Williamson, B. 2015 · 2015
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Deep learning with differential privacy
Abadi, M.; Chu, A.; Goodfellow, I.; McMahan, H. B.; Mironov, I.; Talwar, K.; and Zhang, L. 2016 · 2016
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Differentially Private Regression Diagnostics
Chen, Y.; Machanavajjhala, A.; Reiter, J. P.; and Barrientos, A. F. 2016 · 2016
Cited alongside, same era.
Wide & deep learning for recommender systems
Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; et al. 2016 · 2016
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K.; Ivanov, V.; Kreuter, B.; Marcedone, A.; McMahan, H. B.; Patel, S.; Ramage, D.; Segal, A.; and Seth, K. 2017 · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B.; Moore, E.; Ramage, D.; Hampson, S.; and y Arcas, B. A. 2017 · 2017
Cited alongside, same era.
A formal foundation for secure remote execution of enclaves
Subramanyan, P.; Sinha, R.; Lebedev, I.; Devadas, S.; and Seshia, S. A. 2017 · 2017
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
An Efficient Framework for Clustered Federated Learning
Ghosh, A.; Chung, J.; Yin, D.; and Ramchandran, K. 2020 · 2020
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FedBoost: A Communication-Efficient Algorithm for Federated Learning
Hamer, J.; Mohri, M.; and Suresh, A. T. 2020 · 2020
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Lower Bounds and Optimal Algorithms for Personalized Federated Learning
Hanzely, F.; Hanzely, S.; Horváth, S.; and Richtarik, P. 2020 · 2020
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SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
Karimireddy, S. P.; Kale, S.; Mohri, M.; Reddi, S.; Stich, S.; and Suresh, A. T. 2020 · 2020
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Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization
Li, Z.; Kovalev, D.; Qian, X.; and Richtarik, P. 2020 · 2020
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A Comprehensive Survey on Local Differential Privacy
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Gupta, O.; and Raskar, R. 2018 · 2018
Cited alongside, same era.
Learning Differentially Private Recurrent Language Models
McMahan, H. B.; Ramage, D.; Talwar, K.; and Zhang, L. 2018 · 2018
Cited alongside, same era.
Distributed differential privacy via shuffling
Cheu, A.; Smith, A.; Ullman, J.; Zeber, D.; and Zhilyaev, M. 2019 · 2019
Cited alongside, same era.
Amplification by shuffling: From local to central differential privacy via anonymity
Erlingsson, Ú.; Feldman, V.; Mironov, I.; Raghunathan, A.; Talwar, K.; and Thakurta, A. 2019 · 2019
Cited alongside, same era.
Reducing leakage in distributed deep learning for sensitive health data
Vepakomma, P.; Gupta, O.; Dubey, A.; and Raskar, R. 2019 · 2019
Cited alongside, same era.
Deep leakage from gradients
Zhu, L.; Liu, Z.; and Han, S. 2019 · 2019
Cited alongside, same era.
Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?
Abuadbba, S.; Kim, K.; Kim, M.; Thapa, C.; Camtepe, S. A.; Gao, Y.; Kim, H.; and Nepal, S. 2020 · 2020
Cited alongside, same era.
Xiong, X.; Liu, S.; Li, D.; Cai, Z.; Niu, X.; and Del Rey, A. M. 2020 · 2020
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Federated Accelerated Stochastic Gradient Descent
Yuan, H.; and Ma, T. 2020 · 2020
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On Deep Learning with Label Differential Privacy
Ghazi, B.; Golowich, N.; Kumar, R.; Manurangsi, P.; and Zhang, C. 2021 · 2021
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Numerical Composition of Differential Privacy
Gopi, S.; Lee, Y. T.; and Wutschitz, L. 2021 · 2021
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Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications
Ma, F.; and Wang, P. 2021 · 2021
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Defending against Reconstruction Attack in Vertical Federated Learning
Sun, J.; Yao, Y.; Gao, W.; Xie, J.; and Wang, C. 2021 · 2021
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Label Leakage and Protection in Two-party Split Learning
Li, O.; Sun, J.; Yang, X.; Gao, W.; Zhang, H.; Xie, J.; Smith, V.; and Wang, C. 2022 · 2022
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